From 1cb522e54b0b37772d170050c2508cb6acf25257 Mon Sep 17 00:00:00 2001 From: Colin Catlin Date: Mon, 7 Aug 2023 16:04:08 -0500 Subject: [PATCH] 0.6.0 --- README.md | 2 +- TODO.md | 2 +- autots/__init__.py | 2 +- autots/evaluator/anomaly_detector.py | 7 +- autots/evaluator/auto_model.py | 23 +- autots/evaluator/auto_ts.py | 200 +++++++++++++----- autots/models/basics.py | 10 +- autots/models/cassandra.py | 99 +++++---- autots/models/gluonts.py | 7 +- autots/models/model_list.py | 12 +- autots/models/sklearn.py | 4 +- autots/tools/impute.py | 70 ++++-- autots/tools/seasonal.py | 6 +- autots/tools/transform.py | 113 +++++++--- docs/build/doctrees/environment.pickle | Bin 8269808 -> 8417825 bytes docs/build/doctrees/source/autots.doctree | Bin 824132 -> 866790 bytes .../doctrees/source/autots.evaluator.doctree | Bin 822676 -> 854426 bytes .../doctrees/source/autots.models.doctree | Bin 2123501 -> 2149660 bytes .../doctrees/source/autots.templates.doctree | Bin 87943 -> 16063 bytes .../doctrees/source/autots.tools.doctree | Bin 1393687 -> 1439658 bytes docs/build/doctrees/source/intro.doctree | Bin 43247 -> 43251 bytes docs/build/doctrees/source/tutorial.doctree | Bin 200882 -> 201426 bytes docs/build/html/.buildinfo | 2 +- .../html/_static/documentation_options.js | 2 +- docs/build/html/genindex.html | 100 ++++++++- docs/build/html/index.html | 2 +- docs/build/html/objects.inv | Bin 6532 -> 6719 bytes docs/build/html/py-modindex.html | 2 +- docs/build/html/search.html | 2 +- docs/build/html/searchindex.js | 2 +- docs/build/html/source/autots.datasets.html | 2 +- docs/build/html/source/autots.evaluator.html | 93 +++++++- docs/build/html/source/autots.html | 131 +++++++++++- docs/build/html/source/autots.models.html | 60 +++++- docs/build/html/source/autots.templates.html | 22 +- docs/build/html/source/autots.tools.html | 105 ++++++++- docs/build/html/source/intro.html | 4 +- docs/build/html/source/modules.html | 10 +- docs/build/html/source/tutorial.html | 8 +- docs/conf.py | 2 +- extended_tutorial.md | 1 + production_example.py | 40 ++-- pyproject.toml | 2 +- setup.py | 2 +- test.py | 72 +++++-- tests/test_autots.py | 3 +- 46 files changed, 950 insertions(+), 276 deletions(-) diff --git a/README.md b/README.md index d75199f4..1a2286fe 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ model = AutoTS( forecast_length=21, frequency='infer', prediction_interval=0.9, - ensemble=None, + ensemble='auto', model_list="fast", # "superfast", "default", "fast_parallel" transformer_list="fast", # "superfast", drop_most_recent=1, diff --git a/TODO.md b/TODO.md index 422c8b5b..5e0d55fc 100644 --- a/TODO.md +++ b/TODO.md @@ -12,7 +12,7 @@ * The most recent data will generally be the most important * Forecasts are desired for the future immediately following the most recent data. -# 0.5.9 ♨️♨️♨️ +# 0.6.0 ♨️♨️♨️ * import_best_model and fit_data * added canberra distance to SeasonalityMotif and MetricMotif * DatepartRegressionTransformer now handles NaN in input data diff --git a/autots/__init__.py b/autots/__init__.py index 330d8fad..6f1aa92a 100644 --- a/autots/__init__.py +++ b/autots/__init__.py @@ -26,7 +26,7 @@ from autots.models.cassandra import Cassandra -__version__ = '0.5.9' +__version__ = '0.6.0' TransformTS = GeneralTransformer diff --git a/autots/evaluator/anomaly_detector.py b/autots/evaluator/anomaly_detector.py index 8a3d395d..7abc1c8b 100644 --- a/autots/evaluator/anomaly_detector.py +++ b/autots/evaluator/anomaly_detector.py @@ -319,7 +319,12 @@ def plot_anomaly(self, kwargs={}): self.anomaly_model.plot(**kwargs) def plot( - self, series_name=None, include_anomalies=True, title=None, plot_kwargs={}, series=None + self, + series_name=None, + include_anomalies=True, + title=None, + plot_kwargs={}, + series=None, ): import matplotlib.pyplot as plt diff --git a/autots/evaluator/auto_model.py b/autots/evaluator/auto_model.py index 8bb0a0c9..3465eb59 100644 --- a/autots/evaluator/auto_model.py +++ b/autots/evaluator/auto_model.py @@ -733,7 +733,9 @@ def fit(self, df, future_regressor=None): f, ) except Exception as e: - error_msg = f"failed to write {self.current_model_file} with error {repr(e)}" + error_msg = ( + f"failed to write {self.current_model_file} with error {repr(e)}" + ) try: with open(f'{self.current_model_file}_failure.json', 'w') as f: f.write(error_msg) @@ -749,7 +751,9 @@ def fit(self, df, future_regressor=None): self.transformation_runtime = datetime.datetime.now() - transformationStartTime # from autots.evaluator.auto_model import ModelMonster - self.model = self.model.fit(df_train_transformed, future_regressor=future_regressor) + self.model = self.model.fit( + df_train_transformed, future_regressor=future_regressor + ) self._fit_complete = True return self @@ -790,7 +794,9 @@ def predict(self, forecast_length=None, future_regressor=None): ) # CHECK Forecasts are proper length! if df_forecast.forecast.shape[0] != self.forecast_length: - raise ValueError(f"Model {self.model_str} returned improper forecast_length") + raise ValueError( + f"Model {self.model_str} returned improper forecast_length" + ) if df_forecast.forecast.shape[1] != self.df.shape[1]: raise ValueError("Model failed to return correct number of series.") @@ -806,7 +812,9 @@ def predict(self, forecast_length=None, future_regressor=None): if self.constraint is not None: if isinstance(self.constraint, dict): constraint_method = self.constraint.get("constraint_method", "quantile") - constraint_regularization = self.constraint.get("constraint_regularization", 1) + constraint_regularization = self.constraint.get( + "constraint_regularization", 1 + ) lower_constraint = self.constraint.get("lower_constraint", 0) upper_constraint = self.constraint.get("upper_constraint", 1) bounds = self.constraint.get("bounds", False) @@ -850,13 +858,12 @@ def predict(self, forecast_length=None, future_regressor=None): sys.stdout.flush() return df_forecast - + def fit_data(self, df, future_regressor=None): self.df = df self.model.fit_data(df, future_regressor) - class TemplateEvalObject(object): """Object to contain all the failures!. @@ -1279,7 +1286,9 @@ def model_forecast( model_count=model_count, ) model = model.fit(df_train_low, future_regressor_train) - return model.predict(forecast_length, future_regressor=future_regressor_forecast) + return model.predict( + forecast_length, future_regressor=future_regressor_forecast + ) def _ps_metric(per_series_metrics, metric, model_id): diff --git a/autots/evaluator/auto_ts.py b/autots/evaluator/auto_ts.py index 12f44ce7..5a3b8cd7 100644 --- a/autots/evaluator/auto_ts.py +++ b/autots/evaluator/auto_ts.py @@ -636,10 +636,33 @@ def get_new_params(method='random'): '2': 'ClipOutliers', }, 'transformation_params': { - '0': {'method': 'remove', 'std_threshold': 2.5, 'fillna': None}, # "SeasonalityMotifImputerLinMix" - '1': {"sigma": 2, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": ['common_fourier'], "polynomial_degree": None, "transform_dict": None, "holiday_countries_used": False}, "holiday_params": None}, - '2': {'method': 'remove', 'std_threshold': 3.0, 'fillna': "SeasonalityMotifImputerLinMix"}, - } + '0': { + 'method': 'remove', + 'std_threshold': 2.5, + 'fillna': None, + }, # "SeasonalityMotifImputerLinMix" + '1': { + "sigma": 2, + "rolling_window": 90, + "run_order": "season_first", + "regression_params": { + "regression_model": { + "model": "ElasticNet", + "model_params": {}, + }, + "datepart_method": ['common_fourier'], + "polynomial_degree": None, + "transform_dict": None, + "holiday_countries_used": False, + }, + "holiday_params": None, + }, + '2': { + 'method': 'remove', + 'std_threshold': 3.0, + 'fillna': "SeasonalityMotifImputerLinMix", + }, + }, }, { 'fillna': None, @@ -650,11 +673,40 @@ def get_new_params(method='random'): '3': 'ClipOutliers', }, 'transformation_params': { - '0': {'method': 'remove', 'std_threshold': 2.5, 'fillna': None}, # "SeasonalityMotifImputerLinMix" - '1': {'window_size': 90, 'alpha': 2.5, 'grouping_forward_limit': 3, 'max_level_shifts': 5, 'alignment': 'average'}, - '2': {"sigma": 2, "rolling_window": 90, "run_order": "season_first", "regression_params": {"regression_model": {"model": "ElasticNet", "model_params": {}}, "datepart_method": ['common_fourier'], "polynomial_degree": None, "transform_dict": None, "holiday_countries_used": False}, "holiday_params": None}, - '3': {'method': 'remove', 'std_threshold': 3.0, 'fillna': "SeasonalityMotifImputerLinMix"}, - } + '0': { + 'method': 'remove', + 'std_threshold': 2.5, + 'fillna': None, + }, # "SeasonalityMotifImputerLinMix" + '1': { + 'window_size': 90, + 'alpha': 2.5, + 'grouping_forward_limit': 3, + 'max_level_shifts': 5, + 'alignment': 'average', + }, + '2': { + "sigma": 2, + "rolling_window": 90, + "run_order": "season_first", + "regression_params": { + "regression_model": { + "model": "ElasticNet", + "model_params": {}, + }, + "datepart_method": ['common_fourier'], + "polynomial_degree": None, + "transform_dict": None, + "holiday_countries_used": False, + }, + "holiday_params": None, + }, + '3': { + 'method': 'remove', + 'std_threshold': 3.0, + 'fillna': "SeasonalityMotifImputerLinMix", + }, + }, }, { "fillna": None, @@ -662,11 +714,11 @@ def get_new_params(method='random'): "transformation_params": { "0": { 'rolling_window': 30, - 'n_tails': 0.1, - 'n_future': 0.2, - 'method': 'mean', - 'macro_micro': True - }, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True, + }, }, }, 'random', @@ -830,7 +882,15 @@ def __repr__(self): except Exception: return "Initiated AutoTS object" - def fit_data(self, df, date_col=None, value_col=None, id_col=None, future_regressor=None, weights={}): + def fit_data( + self, + df, + date_col=None, + value_col=None, + id_col=None, + future_regressor=None, + weights={}, + ): """Part of the setup that involves fitting the initial data but not running any models.""" self.date_col = date_col self.value_col = value_col @@ -895,7 +955,7 @@ def fit_data(self, df, date_col=None, value_col=None, id_col=None, future_regres ens_piece3 = "" # self.ensemble = ens_piece1 + "," + ens_piece2 + "," + ens_piece3 self.ensemble = [ens_piece1, ens_piece2, ens_piece3] - + # because horizontal cannot handle non-string columns/series_ids if any(x in self.ensemble for x in self.h_ens_list): df_wide_numeric.columns = [str(xc) for xc in df_wide_numeric.columns] @@ -940,7 +1000,7 @@ def fit_data(self, df, date_col=None, value_col=None, id_col=None, future_regres self.df_wide_numeric = df_wide_numeric self.startTimeStamps = df_wide_numeric.notna().idxmax() - + if future_regressor is not None: if not isinstance(future_regressor, pd.DataFrame): future_regressor = pd.DataFrame(future_regressor) @@ -960,7 +1020,9 @@ def fit_data(self, df, date_col=None, value_col=None, id_col=None, future_regres # handle any non-numeric data, crudely self.regr_num_trans = NumericTransformer(verbose=self.verbose) - self.future_regressor_train = self.regr_num_trans.fit_transform(future_regressor) + self.future_regressor_train = self.regr_num_trans.fit_transform( + future_regressor + ) # check how many validations are possible given the length of the data. self.num_validations = validate_num_validations( @@ -984,7 +1046,7 @@ def fit_data(self, df, date_col=None, value_col=None, id_col=None, future_regres preclean=None, verbose=0, ) - + def fit( self, df, @@ -1056,10 +1118,14 @@ def fit( random_seed = abs(int(random_seed)) random.seed(random_seed) np.random.seed(random_seed) - + self.fit_data( - df=df, date_col=date_col, value_col=value_col, id_col=id_col, - future_regressor=future_regressor, weights=weights + df=df, + date_col=date_col, + value_col=value_col, + id_col=id_col, + future_regressor=future_regressor, + weights=weights, ) ensemble = self.ensemble @@ -1109,8 +1175,12 @@ def fit( self.validation_train_indexes.append(df_train.index) self.validation_test_indexes.append(df_test.index) if future_regressor is not None: - future_regressor_train = self.future_regressor_train.reindex(index=df_train.index) - future_regressor_test = self.future_regressor_train.reindex(index=df_test.index) + future_regressor_train = self.future_regressor_train.reindex( + index=df_train.index + ) + future_regressor_test = self.future_regressor_train.reindex( + index=df_test.index + ) else: future_regressor_train = None future_regressor_test = None @@ -1667,11 +1737,12 @@ def fit( if eligible_models.empty: # this may occur if there is enough data for full validations # but a lot of that data is bad leading to complete validation round failures - print("your validation results are questionable, perhaps bad data and too many validations") + print( + "your validation results are questionable, perhaps bad data and too many validations" + ) max_vals = self.validation_results.model_results['Runs'].max() eligible_models = self.validation_results.model_results[ - self.validation_results.model_results['Runs'] - >= max_vals + self.validation_results.model_results['Runs'] >= max_vals ] try: self.best_model_non_horizontal = ( @@ -1713,11 +1784,12 @@ def fit( if eligible_models.empty: # this may occur if there is enough data for full validations # but a lot of that data is bad leading to complete validation round failures - print("your validation results are questionable, perhaps bad data and too many validations") + print( + "your validation results are questionable, perhaps bad data and too many validations" + ) max_vals = self.validation_results.model_results['Runs'].max() eligible_models = self.validation_results.model_results[ - self.validation_results.model_results['Runs'] - >= max_vals + self.validation_results.model_results['Runs'] >= max_vals ] try: self.best_model = ( @@ -1735,10 +1807,12 @@ def fit( # clean up any remaining print statements sys.stdout.flush() return self - + def parse_best_model(self): if self.best_model.empty: - raise ValueError("no best model present. Run .fit() of the AutoTS class first.") + raise ValueError( + "no best model present. Run .fit() of the AutoTS class first." + ) self.best_model_name = self.best_model['Model'].iloc[0] self.best_model_id = self.best_model['ID'].iloc[0] self.best_model_params = json.loads(self.best_model['ModelParameters'].iloc[0]) @@ -1959,10 +2033,20 @@ def _predict( refit=False, ): use_model = self.best_model_name if model_name is None else model_name - use_params = self.best_model_params.copy() if model_params is None else model_params - use_trans = self.best_model_transformation_params if model_transformation_params is None else model_transformation_params + use_params = ( + self.best_model_params.copy() if model_params is None else model_params + ) + use_trans = ( + self.best_model_transformation_params + if model_transformation_params is None + else model_transformation_params + ) use_data = self.df_wide_numeric if df_wide_numeric is None else df_wide_numeric - use_regr_train = self.future_regressor_train if future_regressor_train is None else future_regressor_train + use_regr_train = ( + self.future_regressor_train + if future_regressor_train is None + else future_regressor_train + ) if use_model in update_fit: if self.model is None or refit: self.model = ModelPrediction( @@ -1987,7 +2071,9 @@ def _predict( self.model = self.model.fit(use_data, future_regressor=use_regr_train) else: self.model.fit_data(use_data, future_regressor=use_regr_train) - df_forecast = self.model.predict(forecast_length, future_regressor=future_regressor) + df_forecast = self.model.predict( + forecast_length, future_regressor=future_regressor + ) else: df_forecast = model_forecast( model_name=use_model, @@ -2220,14 +2306,16 @@ def export_template( else: raise ValueError("`models` must be 'all' or 'best'") return self.save_template(filename, export_template) - + def save_template(self, filename, export_template, **kwargs): """Helper function for the save part of export_template.""" try: if filename is None: return export_template elif '.csv' in filename: - return export_template.to_csv(filename, index=False, **kwargs) # lineterminator='\r\n' + return export_template.to_csv( + filename, index=False, **kwargs + ) # lineterminator='\r\n' elif '.json' in filename: return export_template.to_json(filename, orient='columns', **kwargs) else: @@ -2257,8 +2345,10 @@ def load_template(self, filename): ) ) return import_template - - def _enforce_model_list(self, template, model_list=None, include_ensemble=False, addon_flag=False): + + def _enforce_model_list( + self, template, model_list=None, include_ensemble=False, addon_flag=False + ): """remove models not in given model list.""" if model_list is None: model_list = self.model_list @@ -2309,8 +2399,10 @@ def import_template( if enforce_model_list: import_template = self._enforce_model_list( - template=import_template, model_list=None, - include_ensemble=include_ensemble, addon_flag=addon_flag + template=import_template, + model_list=None, + include_ensemble=include_ensemble, + addon_flag=addon_flag, ) if addon_flag: @@ -2335,9 +2427,14 @@ def export_best_model(self, filename, **kwargs): """Basically the same as export_template but only ever the one best model.""" return self.save_template(filename, self.best_model.copy(), **kwargs) - def import_best_model(self, import_target, enforce_model_list: bool = True, include_ensemble: bool = True): + def import_best_model( + self, + import_target, + enforce_model_list: bool = True, + include_ensemble: bool = True, + ): """Load a best model, overriding any existing setting. - + Args: import_target: pd.DataFrame or file path """ @@ -2351,12 +2448,14 @@ def import_best_model(self, import_target, enforce_model_list: bool = True, incl ) if enforce_model_list: template = self._enforce_model_list( - template=template, model_list=None, - include_ensemble=include_ensemble, addon_flag=False + template=template, + model_list=None, + include_ensemble=include_ensemble, + addon_flag=False, ) self.best_model = template.iloc[0:1] - + self.parse_best_model() return self @@ -2568,7 +2667,12 @@ def horizontal_to_df(self): series.set_index(series.columns[0], inplace=True) series = series.mode(axis=1)[0].to_frame().reset_index(drop=False) series.columns = ['Series', 'ID'] - results = pd.Series({x: self.best_model_params['models'][x]['Model'] for x in self.best_model_params['models'].keys()}) + results = pd.Series( + { + x: self.best_model_params['models'][x]['Model'] + for x in self.best_model_params['models'].keys() + } + ) results.name = "Model" series = series.merge(results, left_on="ID", right_index=True) # series = series.merge(self.results()[['ID', "Model"]].drop_duplicates(), on="ID") # old diff --git a/autots/models/basics.py b/autots/models/basics.py index 1120f9f1..5e96d2be 100644 --- a/autots/models/basics.py +++ b/autots/models/basics.py @@ -8,7 +8,11 @@ import numpy as np import pandas as pd from autots.models.base import ModelObject, PredictionObject -from autots.tools.seasonal import seasonal_int, seasonal_window_match, seasonal_independent_match +from autots.tools.seasonal import ( + seasonal_int, + seasonal_window_match, + seasonal_independent_match, +) from autots.tools.probabilistic import Point_to_Probability, historic_quantile from autots.tools.window_functions import window_id_maker, sliding_window_view from autots.tools.percentile import nan_quantile @@ -2489,7 +2493,7 @@ def predict( else: scores = np.mean(np.abs(temp - last_window.T), axis=2) elif distance_metric == "canberra": - divisor = (np.abs(temp) + np.abs(last_window.T)) + divisor = np.abs(temp) + np.abs(last_window.T) divisor[divisor == 0] = 1 if nan_flag: scores = np.nanmean(np.abs(temp - last_window.T) / divisor, axis=2) @@ -2840,7 +2844,7 @@ def get_new_params(self, method: str = 'random'): ], [0.4, 0.3, 0.3, 0.3, 0.4, 0.35, 0.45, 0.2], )[0], - "independent": bool(random.getrandbits(1)) + "independent": bool(random.getrandbits(1)), } def get_params(self): diff --git a/autots/models/cassandra.py b/autots/models/cassandra.py index 393a0cf8..949a6e10 100644 --- a/autots/models/cassandra.py +++ b/autots/models/cassandra.py @@ -1196,26 +1196,34 @@ def _predict_step( return df_forecast def fit_data( - self, df, - forecast_length=None, - future_regressor=None, - regressor_per_series=None, - flag_regressors=None, - future_impacts=None, - regressor_forecast_model=None, - regressor_forecast_model_params=None, - regressor_forecast_transformations=None, - include_history=True, - past_impacts=None, - ): + self, + df, + forecast_length=None, + future_regressor=None, + regressor_per_series=None, + flag_regressors=None, + future_impacts=None, + regressor_forecast_model=None, + regressor_forecast_model_params=None, + regressor_forecast_transformations=None, + include_history=True, + past_impacts=None, + ): self.df = self.preprocesser.transform(self.df) if self.past_impacts_intervention == "remove": try: self.df = self.df / (1 + past_impacts) except TypeError: - raise ValueError("if using past impact with df updates, must pass past_impacts to .fit_data or .predict") + raise ValueError( + "if using past impact with df updates, must pass past_impacts to .fit_data or .predict" + ) self.df = self.scale_data(df) - self.regr_ps_fore, self.use_impacts, self.full_regr, self.all_flags = self._process_regressors( + ( + self.regr_ps_fore, + self.use_impacts, + self.full_regr, + self.all_flags, + ) = self._process_regressors( df=self.df, forecast_length=forecast_length, future_regressor=future_regressor, @@ -1239,21 +1247,23 @@ def fit_data( self.trend_train, slope, intercept = self.rolling_trend( self.trend_train, np.array(self.create_t(df.index)) ) - self.trend_train = pd.DataFrame(self.trend_train, index=df.index, columns=df.columns) + self.trend_train = pd.DataFrame( + self.trend_train, index=df.index, columns=df.columns + ) def _process_regressors( - self, - df=None, - forecast_length=None, - future_regressor=None, - regressor_per_series=None, - flag_regressors=None, - future_impacts=None, - regressor_forecast_model=None, - regressor_forecast_model_params=None, - regressor_forecast_transformations=None, - include_history=True, - ): + self, + df=None, + forecast_length=None, + future_regressor=None, + regressor_per_series=None, + flag_regressors=None, + future_impacts=None, + regressor_forecast_model=None, + regressor_forecast_model_params=None, + regressor_forecast_transformations=None, + include_history=True, + ): # if future regressors are None (& USED), but were provided for history, instead use forecasts of these features (warn) full_regr = None if ( @@ -1387,7 +1397,7 @@ def predict( future_regressor and regressor_per_series should only include new future values, history is already stored they should match on forecast_length and index of forecasts - + Args: forecast_length (int): steps ahead to predict, or None include_history (bool): include past predictions if True @@ -1405,20 +1415,25 @@ def predict( new_df = df if new_df is not None: self.fit_data( - df=new_df, - forecast_length=forecast_length, - future_regressor=future_regressor, - regressor_per_series=regressor_per_series, - flag_regressors=flag_regressors, - future_impacts=future_impacts, - regressor_forecast_model=regressor_forecast_model, - regressor_forecast_model_params=regressor_forecast_model_params, - regressor_forecast_transformations=regressor_forecast_transformations, - include_history=include_history, - past_impacts=past_impacts - ) + df=new_df, + forecast_length=forecast_length, + future_regressor=future_regressor, + regressor_per_series=regressor_per_series, + flag_regressors=flag_regressors, + future_impacts=future_impacts, + regressor_forecast_model=regressor_forecast_model, + regressor_forecast_model_params=regressor_forecast_model_params, + regressor_forecast_transformations=regressor_forecast_transformations, + include_history=include_history, + past_impacts=past_impacts, + ) else: - self.regr_ps_fore, self.use_impacts, self.full_regr, self.all_flags = self._process_regressors( + ( + self.regr_ps_fore, + self.use_impacts, + self.full_regr, + self.all_flags, + ) = self._process_regressors( df=self.df, forecast_length=forecast_length, future_regressor=future_regressor, @@ -2129,7 +2144,7 @@ def plot_components( plot_df = pd.concat(plot_list, axis=1) if start_date is not None: if start_date == "auto": - plot_df = plot_df.iloc[-(prediction.forecast_length * 3):] + plot_df = plot_df.iloc[-(prediction.forecast_length * 3) :] else: plot_df = plot_df[plot_df.index >= start_date] return plot_df.plot(subplots=True, figsize=figsize, title=title) diff --git a/autots/models/gluonts.py b/autots/models/gluonts.py index 28751929..057f8447 100644 --- a/autots/models/gluonts.py +++ b/autots/models/gluonts.py @@ -106,7 +106,7 @@ def fit(self, df, future_regressor=None): gluon_freq = str(self.frequency).split('-')[0] self.train_index = df.columns self.train_columns = df.index - + if gluon_freq in ["MS", "1MS"]: gluon_freq = "M" @@ -453,7 +453,10 @@ def fit_data(self, df, future_regressor=None): return self def predict( - self, forecast_length: int = None, future_regressor=[], just_point_forecast=False + self, + forecast_length: int = None, + future_regressor=[], + just_point_forecast=False, ): """Generates forecast data immediately following dates of index supplied to .fit() diff --git a/autots/models/model_list.py b/autots/models/model_list.py index ada709fe..63694fc8 100644 --- a/autots/models/model_list.py +++ b/autots/models/model_list.py @@ -122,7 +122,9 @@ } # models that should be fast given many CPU cores fast_parallel = {**parallel, **fast} -fast_parallel_no_arima = {i: fast_parallel[i] for i in fast_parallel if i not in ['ARIMA', 'NVAR']} +fast_parallel_no_arima = { + i: fast_parallel[i] for i in fast_parallel if i not in ['ARIMA', 'NVAR'] +} # so this opiniated and not fully updated always best = list( set( @@ -310,7 +312,13 @@ "ARCH", ] # models that fit and then have updated predicts without updated model fits (just data update) -update_fit = ['MultivariateRegression', "DatepartRegression", "GluonTS", 'WindowRegression', 'Cassandra'] +update_fit = [ + 'MultivariateRegression', + "DatepartRegression", + "GluonTS", + 'WindowRegression', + 'Cassandra', +] model_lists = { "all": all_models, "default": default, diff --git a/autots/models/sklearn.py b/autots/models/sklearn.py index 151fd267..43205fd0 100644 --- a/autots/models/sklearn.py +++ b/autots/models/sklearn.py @@ -1515,7 +1515,7 @@ def fit_data(self, df, future_regressor=None): def predict( self, - forecast_length: int=None, + forecast_length: int = None, future_regressor=None, just_point_forecast: bool = False, df=None, @@ -1804,7 +1804,7 @@ def fit(self, df, future_regressor=None): def predict( self, - forecast_length: int=None, + forecast_length: int = None, future_regressor=None, just_point_forecast: bool = False, ): diff --git a/autots/tools/impute.py b/autots/tools/impute.py index b4155a4e..942ef212 100644 --- a/autots/tools/impute.py +++ b/autots/tools/impute.py @@ -284,23 +284,30 @@ def FillNA(df, method: str = 'ffill', window: int = 10): elif method == 'SeasonalityMotifImputer': s_imputer = SeasonalityMotifImputer( - k=3, datepart_method="common_fourier", distance_metric="canberra", linear_mixed=False, + k=3, + datepart_method="common_fourier", + distance_metric="canberra", + linear_mixed=False, ) return s_imputer.impute(df) # .rename(lambda x: str(x) + "_motif", axis=1) elif method == 'SeasonalityMotifImputerLinMix': s_imputer = SeasonalityMotifImputer( - k=2, datepart_method="common_fourier", distance_metric="canberra", linear_mixed=True, + k=2, + datepart_method="common_fourier", + distance_metric="canberra", + linear_mixed=True, ) return s_imputer.impute(df) # .rename(lambda x: str(x) + "_motif", axis=1) - elif method == 'DatepartRegressionImputer': # circular import from autots.tools.transform import DatepartRegressionTransformer + imputer = DatepartRegressionTransformer( datepart_method="common_fourier", - holiday_country=["US"], holiday_countries_used=True, + holiday_country=["US"], + holiday_countries_used=True, regression_model={ "model": 'RandomForest', "model_params": { @@ -322,14 +329,15 @@ def FillNA(df, method: str = 'ffill', window: int = 10): class SeasonalityMotifImputer(object): - def __init__(self, + def __init__( + self, k: int = 3, datepart_method: str = "simple_2", distance_metric: str = "canberra", linear_mixed: bool = False, ): """Shares arg params with SeasonalityMotif model with which it has much in common. - + Args: k (int): n neighbors. More is smoother, fewer is most accurate, usually datepart_method (str): standard date part methods accepted @@ -340,7 +348,7 @@ def __init__(self, self.datepart_method = datepart_method self.distance_metric = distance_metric self.linear_mixed = linear_mixed - + def impute(self, df): """Infer missing values on input df.""" test, scores = seasonal_independent_match( @@ -353,34 +361,56 @@ def impute(self, df): full_dist = np.argsort(scores) full_nan_mask = np.isnan(df.to_numpy()) - brdcst_mask = np.broadcast_to(full_nan_mask[..., None], full_nan_mask.shape + (df.shape[0],)).T - brdcst_mask = np.moveaxis(np.broadcast_to(full_nan_mask[..., None], full_nan_mask.shape + (df.shape[0],)), 0, 0) + brdcst_mask = np.broadcast_to( + full_nan_mask[..., None], full_nan_mask.shape + (df.shape[0],) + ).T + brdcst_mask = np.moveaxis( + np.broadcast_to( + full_nan_mask[..., None], full_nan_mask.shape + (df.shape[0],) + ), + 0, + 0, + ) # brdcst = np.array(np.broadcast_to(full_dist[...,None],full_dist.shape+(df.shape[1],))) # .reshape(brdcst_mask.shape) - brdcst = np.moveaxis(np.array(np.broadcast_to(full_dist[...,None],full_dist.shape+(df.shape[1],))), -1, 1) + brdcst = np.moveaxis( + np.array( + np.broadcast_to(full_dist[..., None], full_dist.shape + (df.shape[1],)) + ), + -1, + 1, + ) # mask_positive = (np.cumsum(~brdcst_mask, axis=-1) <= k) & ~brdcst_mask # True = keeps # mask_negative = (np.cumsum(~brdcst_mask, axis=-1) > k) | brdcst_mask # True = don't keep - mask_negative = (np.cumsum(~brdcst_mask, axis=-1) > self.k) # True = don't keep + mask_negative = np.cumsum(~brdcst_mask, axis=-1) > self.k # True = don't keep # test = np.ma.masked_array(brdcst.T, mask_negative) # temp = np.take(df.to_numpy()[..., None], brdcst) # arrd = np.take(df.to_numpy().T, brdcst).T - arrd = np.take_along_axis(np.broadcast_to(df.to_numpy()[...,None], df.shape + (df.shape[0],)), brdcst, axis=0) + arrd = np.take_along_axis( + np.broadcast_to(df.to_numpy()[..., None], df.shape + (df.shape[0],)), + brdcst, + axis=0, + ) arrd_mask = np.isnan(arrd) - mask_negative = (np.cumsum(~arrd_mask, axis=-1) > self.k) # True = don't keep + mask_negative = np.cumsum(~arrd_mask, axis=-1) > self.k # True = don't keep arrd[arrd_mask] = 0 temp = np.ma.masked_array(arrd, mask_negative) test = (temp.sum(axis=2) / self.k).data self.df_impt = pd.DataFrame(test, index=df.index, columns=df.columns) if self.linear_mixed: - self.df_impt = self.df_impt - 0.5 * (self.df_impt.rolling(14, min_periods=1, center=True).mean() - df.interpolate("linear")) - + self.df_impt = self.df_impt - 0.5 * ( + self.df_impt.rolling(14, min_periods=1, center=True).mean() + - df.interpolate("linear") + ) + # col = "US__sv_feed_interface" # pd.concat([df.loc[:, col], df_impt.loc[:, col + "_imputed"]], axis=1).plot() - + return df.where(~full_nan_mask, self.df_impt) + # accuracy test (not necessarily a test of "best") if False: from autots import load_daily @@ -389,11 +419,13 @@ def impute(self, df): df_daily = load_daily(long=False) start = -400 end = -300 - test = df_daily.iloc[start: end].copy() - df_daily.iloc[start: end] = np.nan + test = df_daily.iloc[start:end].copy() + df_daily.iloc[start:end] = np.nan impute_mape = na_probs.copy() impute_mape = {**impute_mape, **df_interpolate} for key in impute_mape.keys(): df_imputed = FillNA(df_daily, method=key, window=10) - impute_mape[key] = ((df_imputed.iloc[start: end] - test).abs().mean() / df_daily.mean()).mean() + impute_mape[key] = ( + (df_imputed.iloc[start:end] - test).abs().mean() / df_daily.mean() + ).mean() impute_mape diff --git a/autots/tools/seasonal.py b/autots/tools/seasonal.py index e12d32a7..f6bd1743 100644 --- a/autots/tools/seasonal.py +++ b/autots/tools/seasonal.py @@ -494,7 +494,7 @@ def seasonal_window_match( if distance_metric == "mae": scores = np.mean(np.abs(temp - last_window.T), axis=2) elif distance_metric == "canberra": - divisor = (np.abs(temp) + np.abs(last_window.T)) + divisor = np.abs(temp) + np.abs(last_window.T) divisor[divisor == 0] = 1 scores = np.mean(np.abs(temp - last_window.T) / divisor, axis=2) elif distance_metric == "mqae": @@ -530,7 +530,7 @@ def seasonal_window_match( def seasonal_independent_match( - DTindex, DTindex_future, k, datepart_method, distance_metric + DTindex, DTindex_future, k, datepart_method, distance_metric ): array = date_part(DTindex, method=datepart_method).to_numpy() future_array = date_part(DTindex_future, method=datepart_method).to_numpy() @@ -543,7 +543,7 @@ def seasonal_independent_match( if distance_metric == "mae": scores = np.mean(np.abs(a - b), axis=2) elif distance_metric == "canberra": - divisor = (np.abs(a) + np.abs(b)) + divisor = np.abs(a) + np.abs(b) divisor[divisor == 0] = 1 scores = np.mean(np.abs(a - b) / divisor, axis=2) elif distance_metric == "mqae": diff --git a/autots/tools/transform.py b/autots/tools/transform.py index 7a82c616..3b480948 100644 --- a/autots/tools/transform.py +++ b/autots/tools/transform.py @@ -1176,7 +1176,9 @@ def fit(self, df, regressor=None): [ X, holiday_flag( - df_local.index, country=self.holiday_country, encode_holiday_type=True + df_local.index, + country=self.holiday_country, + encode_holiday_type=True, ), ], axis=1, @@ -1189,7 +1191,9 @@ def fit(self, df, regressor=None): X = np.repeat(X.to_numpy(), y.shape[1], axis=0) # add a (hopefully not too massive) series ID encoder. This will not scale well to large multivariate # X = np.concatenate([X, np.repeat(np.eye(y.shape[1]), df_local.shape[0], axis=0)], axis=1) - X = np.concatenate([X, np.tile(np.eye(y.shape[1]), df_local.shape[0]).T], axis=1) + X = np.concatenate( + [X, np.tile(np.eye(y.shape[1]), df_local.shape[0]).T], axis=1 + ) y = y.flatten() y_mask = np.isnan(y) y = y[~y_mask] @@ -1212,7 +1216,7 @@ def fit(self, df, regressor=None): self.model = self.model.fit(X, y) self.shape = df_local.shape return self - + def impute(self, df, regressor=None): """Fill Missing. Needs to have same general pattern of missingness (full rows of NaN only or scattered NaN) as was present during .fit()""" X = date_part( @@ -1235,7 +1239,9 @@ def impute(self, df, regressor=None): if self.partial_nan_rows: # process into a single output approach X = np.repeat(X.to_numpy(), self.shape[1], axis=0) - X = np.concatenate([X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1) + X = np.concatenate( + [X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1 + ) pred = self.model.predict(X) if self.partial_nan_rows: @@ -1284,7 +1290,9 @@ def transform(self, df, regressor=None): if self.partial_nan_rows: # process into a single output approach X = np.repeat(X.to_numpy(), self.shape[1], axis=0) - X = np.concatenate([X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1) + X = np.concatenate( + [X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1 + ) # X.columns = [str(xc) for xc in X.columns] pred = self.model.predict(X) if self.partial_nan_rows: @@ -1324,7 +1332,9 @@ def inverse_transform(self, df, regressor=None): if self.partial_nan_rows: # process into a single output approach X = np.repeat(X.to_numpy(), self.shape[1], axis=0) - X = np.concatenate([X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1) + X = np.concatenate( + [X, np.tile(np.eye(self.shape[1]), df.shape[0]).T], axis=1 + ) pred = self.model.predict(X) if self.partial_nan_rows: pred = pred.reshape(-1, df.shape[1]) @@ -3203,7 +3213,8 @@ def _fit(self, df): if self.macro_micro: macro = pd.DataFrame( self.slope * self.dates_2d + self.intercept, - index=df.index, columns=df.columns + index=df.index, + columns=df.columns, ) self.columns = df.columns micro = (df - macro).rename(columns=lambda x: str(x) + self.suffix) @@ -3234,7 +3245,8 @@ def transform(self, df): if self.macro_micro: macro = pd.DataFrame( self.full_slope[idx] * dates_2d + self.full_intercept[idx], - index=df.index, columns=df.columns + index=df.index, + columns=df.columns, ) micro = (df - macro).rename(columns=lambda x: str(x) + self.suffix) return pd.concat([macro, micro], axis=1) @@ -3250,7 +3262,9 @@ def inverse_transform(self, df, trans_method: str = "forecast"): if self.macro_micro: macro = df[self.columns] micro = df[df.columns.difference(self.columns)] - micro = micro.rename(columns=lambda x: str(x)[:-len(self.suffix)])[self.columns] + micro = micro.rename(columns=lambda x: str(x)[: -len(self.suffix)])[ + self.columns + ] return macro + micro else: dates = df.index.to_julian_date() @@ -3710,7 +3724,9 @@ def get_new_params(method: str = "random"): holiday_params = None return { - "sigma": random.choices([0.5, 1, 1.5, 2, 2.5, 3], [0.1, 0.4, 0.1, 0.3, 0.05, 0.1])[0], + "sigma": random.choices( + [0.5, 1, 1.5, 2, 2.5, 3], [0.1, 0.4, 0.1, 0.3, 0.05, 0.1] + )[0], "rolling_window": 90, "run_order": random.choices(["season_first", "trend_first"], [0.7, 0.3])[0], "regression_params": regression_params, @@ -3746,9 +3762,9 @@ def __init__( @staticmethod def get_new_params(method: str = "random"): return { - "window_size": random.choices( - [7, 30, 90, 364], [0.1, 0.4, 0.4, 0.1], k=1 - )[0], + "window_size": random.choices([7, 30, 90, 364], [0.1, 0.4, 0.4, 0.1], k=1)[ + 0 + ], "alpha": random.choices( [1.0, 2.0, 2.5, 3.0, 3.5, 4.0], [0.05, 0.2, 0.3, 0.2, 0.15, 0.1], k=1 )[0], @@ -3757,7 +3773,7 @@ def get_new_params(method: str = "random"): "alignment": random.choices( ["average", "last_value", "rolling_diff", "rolling_diff_3nn"], [0.5, 0.2, 0.15, 0.15], - )[0] + )[0], } def fit(self, df): @@ -3769,23 +3785,35 @@ def fit(self, df): rolling = df.rolling(self.window_size, center=False, min_periods=1).mean() # pandas 1.1.0 introduction, or thereabouts try: - indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=self.window_size) + indexer = pd.api.indexers.FixedForwardWindowIndexer( + window_size=self.window_size + ) rolling_forward = df.rolling(window=indexer, min_periods=1).mean() except Exception: - rolling_forward = df.loc[::-1].rolling(self.window_size, center=False, min_periods=1).mean()[::-1] + rolling_forward = ( + df.loc[::-1] + .rolling(self.window_size, center=False, min_periods=1) + .mean()[::-1] + ) # compare rolling forward and backwards diffs - diff = (rolling - rolling_forward) - threshold = (diff.std() * self.alpha) + diff = rolling - rolling_forward + threshold = diff.std() * self.alpha diff_abs = diff.abs() - diff_mask_0 = (diff_abs > threshold) # | (diff < -threshold) + diff_mask_0 = diff_abs > threshold # | (diff < -threshold) # merge nearby groups - diff_smoothed = diff_abs.where(diff_mask_0, np.nan).fillna(method='ffill', limit=self.grouping_forward_limit) - diff_mask = (diff_smoothed > threshold) | (diff_smoothed < -threshold) + diff_smoothed = diff_abs.where(diff_mask_0, np.nan).fillna( + method='ffill', limit=self.grouping_forward_limit + ) + diff_mask = (diff_smoothed > threshold) | (diff_smoothed < -threshold) # the max of each changepoint group is the chosen changepoint of the level shift # doesn't handle identical maxes although that is unlikely with these floating point averages maxes = diff_abs.where(diff_mask, np.nan).max() # group ids are needed so that we can progressively remove - range_arr = pd.DataFrame(np.mgrid[0:df.shape[0]:1, 0:df.shape[1]][0], index=df.index, columns=df.columns) + range_arr = pd.DataFrame( + np.mgrid[0 : df.shape[0] : 1, 0 : df.shape[1]][0], + index=df.index, + columns=df.columns, + ) group_ids = range_arr[~diff_mask].fillna(method='ffill') # [diff_mask] max_mask = diff_abs == maxes used_groups = group_ids[max_mask].mean() @@ -3797,19 +3825,43 @@ def fit(self, df): curr_maxes = curr_diff.max() max_mask = max_mask | (curr_diff == curr_maxes) used_groups = group_ids[curr_diff == curr_maxes].mean() - curr_diff = curr_diff.where(((group_ids != used_groups) & diff_mask), np.nan) + curr_diff = curr_diff.where( + ((group_ids != used_groups) & diff_mask), np.nan + ) curr_diff_sum = np.sum(~curr_diff.isnull().to_numpy()) count += 1 # alignment is tricky, especially when level shifts are a mix of gradual and instantaneous if self.alignment == "last_value": - self.lvlshft = (df[max_mask] - df[max_mask.shift(1)].shift(-1)).fillna(0).loc[::-1].cumsum()[::-1] + self.lvlshft = ( + (df[max_mask] - df[max_mask.shift(1)].shift(-1)) + .fillna(0) + .loc[::-1] + .cumsum()[::-1] + ) elif self.alignment == "average": # average the two other approaches - lvlshft1 = (df[max_mask] - df[max_mask.shift(1)].shift(-1)).fillna(0).loc[::-1].cumsum()[::-1] + lvlshft1 = ( + (df[max_mask] - df[max_mask.shift(1)].shift(-1)) + .fillna(0) + .loc[::-1] + .cumsum()[::-1] + ) lvlshft2 = diff[max_mask].fillna(0).loc[::-1].cumsum()[::-1] self.lvlshft = (lvlshft1 + lvlshft2) / 2 elif self.alignment == "rolling_diff_3nn": - self.lvlshft = ((diff[max_mask.shift(1)].shift(-1).fillna(0) + diff[max_mask] + diff[max_mask.shift(-1)].shift(1).fillna(0)) / 3).fillna(0).loc[::-1].cumsum()[::-1] + self.lvlshft = ( + ( + ( + diff[max_mask.shift(1)].shift(-1).fillna(0) + + diff[max_mask] + + diff[max_mask.shift(-1)].shift(1).fillna(0) + ) + / 3 + ) + .fillna(0) + .loc[::-1] + .cumsum()[::-1] + ) else: self.lvlshft = diff[max_mask].fillna(0).loc[::-1].cumsum()[::-1] @@ -3821,7 +3873,9 @@ def transform(self, df): Args: df (pandas.DataFrame): input dataframe """ - return df - self.lvlshft.reindex(index=df.index, columns=df.columns).fillna(method='bfill').fillna(0) + return df - self.lvlshft.reindex(index=df.index, columns=df.columns).fillna( + method='bfill' + ).fillna(0) def inverse_transform(self, df, trans_method: str = "forecast"): """Return data to original *or* forecast form. @@ -3829,7 +3883,9 @@ def inverse_transform(self, df, trans_method: str = "forecast"): Args: df (pandas.DataFrame): input dataframe """ - return df + self.lvlshft.reindex(index=df.index, columns=df.columns).fillna(method='bfill').fillna(0) + return df + self.lvlshft.reindex(index=df.index, columns=df.columns).fillna( + 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  • EnsembleTemplateGenerator() (in module autots.models.ensemble)
  • - - +
  • EWMAFilter (class in autots.tools.transform)
  • +
  • export_best_model() (autots.AutoTS method) + +
  • export_template() (autots.AutoTS method)
  • +
  • fit_data() (autots.AutoTS method) + +
  • fit_sin() (autots.tools.transform.SinTrend static method) @@ -1085,6 +1117,8 @@

    F

  • (autots.tools.transform.IntermittentOccurrence method)
  • (autots.tools.transform.KalmanSmoothing method) +
  • +
  • (autots.tools.transform.LevelShiftMagic method)
  • (autots.tools.transform.LocalLinearTrend method)
  • @@ -1142,8 +1176,6 @@

    G

  • Gaussian (class in autots.tools.fast_kalman)
  • general_template (in module autots.templates.general) -
  • -
  • general_template_dict (in module autots.templates.general)
  • generalize_horizontal() (in module autots.models.ensemble)
  • @@ -1335,6 +1367,8 @@

    G

  • (autots.tools.transform.IntermittentOccurrence static method)
  • (autots.tools.transform.KalmanSmoothing static method) +
  • +
  • (autots.tools.transform.LevelShiftMagic static method)
  • (autots.tools.transform.LocalLinearTrend static method)
  • @@ -1539,6 +1573,12 @@

    H

    I

    + -
    • set_limit_forecast() (in module autots.evaluator.event_forecasting)
    • set_limit_forecast_historic() (in module autots.evaluator.event_forecasting) @@ -2592,6 +2674,8 @@

      T

    • (autots.tools.transform.IntermittentOccurrence method)
    • (autots.tools.transform.KalmanSmoothing method) +
    • +
    • (autots.tools.transform.LevelShiftMagic method)
    • (autots.tools.transform.LocalLinearTrend method)
    • diff --git a/docs/build/html/index.html b/docs/build/html/index.html index 56448bdf..2b4df92a 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -6,7 +6,7 @@ - AutoTS — AutoTS 0.5.8 documentation + AutoTS — AutoTS 0.6.0 documentation diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index 8fc5494f3f2744616a28a628b37f967f6299b6e9..59f0ea5b6cf324f97313b52c50e01e113a5a3533 100644 GIT binary patch delta 6659 zcmV+e8vNyiGru&Dc7IEE=zSKi25coU zHRJW)SXBVIl_DnqcS18Ip4oxtJS_;ILy}PnRzz7Yn9kbLtwEU|5BDonT77@>$fGB= zHBuv9=+)vjkD}1b7@A0ITCR02A4@IPCuc$xB7HQrOynq@k{P`jkSHQ&g~+8SSjrEA zQn^D`Wg{e*&VRWojLuW8Wu_>ljkoMs#)7FbHJOXN7~<<8%QYKY(z>!W2rbe>!nH@` zY`IKI)ADk^)KYsTVlmfzeMQ*+$&+0ajNK+lwC`H`1*?*UV@F-i7@t9DuG%L7_e06IK zv=AiK+ToNDhp3K0MD*>1C0S8;Qu-D{1~irtWu?+|7_}>y^14AroWh= zIKy1UIe&L|jk_e9O*_koTMxbdgMTh&kX8BodHc*-)JxwFP$>S{@bMyt$!pVQUm4GH z{zB!LaZ~3)#onAMwe7^D@@5dr!j&$A?4bcN1u4zgS)$p z%lP#o#nn%h@tZ}4-b=MB{7d50=g9cSS%90DBIBRVq^^xD&*C!jR7jph#^s^*JGn58 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"module-autots.tools.impute"]], "autots.tools.lunar module": [[6, "module-autots.tools.lunar"]], "autots.tools.percentile module": [[6, "module-autots.tools.percentile"]], "autots.tools.probabilistic module": [[6, "module-autots.tools.probabilistic"]], "autots.tools.profile module": [[6, "module-autots.tools.profile"]], "autots.tools.regressor module": [[6, "module-autots.tools.regressor"]], "autots.tools.seasonal module": [[6, "module-autots.tools.seasonal"]], "autots.tools.shaping module": [[6, "module-autots.tools.shaping"]], "autots.tools.thresholding module": [[6, "module-autots.tools.thresholding"]], "autots.tools.transform module": [[6, "module-autots.tools.transform"]], "autots.tools.window_functions module": [[6, "module-autots.tools.window_functions"]], "Intro": [[7, "intro"]], "Table of Contents": [[7, "table-of-contents"], [9, "table-of-contents"]], "Basic Use": [[7, "id2"]], "Tips for Speed and Large Data:": [[7, "id3"]], "How to Contribute:": [[7, "how-to-contribute"]], "autots": [[8, "autots"]], "Tutorial": [[9, "tutorial"]], "Extended Tutorial": [[9, "extended-tutorial"]], "A simple example": [[9, "id1"]], "Import of data": [[9, "import-of-data"]], "You can tailor the process in a few ways\u2026": [[9, "you-can-tailor-the-process-in-a-few-ways"]], "What to Worry About": [[9, "what-to-worry-about"]], "Validation and Cross Validation": [[9, "id2"]], "Another Example:": [[9, "id3"]], "Model Lists": [[9, "id4"]], "Deployment and Template Import/Export": [[9, "deployment-and-template-import-export"]], "Running Just One Model": [[9, "id5"]], "Metrics": [[9, "id6"]], "Hierarchial and Grouped Forecasts": [[9, "hierarchial-and-grouped-forecasts"]], "Ensembles": [[9, "id7"]], "Installation and Dependency Versioning": [[9, "installation-and-dependency-versioning"]], "Requirements:": [[9, "requirements"]], "Optional Packages": [[9, "optional-packages"]], "Safest bet for installation:": [[9, "safest-bet-for-installation"]], "Intel conda channel installation (sometime faster, also, more prone to bugs)": [[9, "intel-conda-channel-installation-sometime-faster-also-more-prone-to-bugs"]], "Speed Benchmark": [[9, "speed-benchmark"]], "Caveats and Advice": [[9, "caveats-and-advice"]], "Mysterious crashes": [[9, "mysterious-crashes"]], "Series IDs really need to be unique (or column names need to be all unique in wide data)": [[9, "series-ids-really-need-to-be-unique-or-column-names-need-to-be-all-unique-in-wide-data"]], "Short Training History": [[9, "short-training-history"]], "Adding regressors and other information": [[9, "adding-regressors-and-other-information"]], "Simulation Forecasting": [[9, "id8"]], "Event Risk Forecasting and Anomaly Detection": [[9, "event-risk-forecasting-and-anomaly-detection"]], "A Hack for Passing in Parameters (that aren\u2019t otherwise available)": [[9, "a-hack-for-passing-in-parameters-that-aren-t-otherwise-available"]], "Categorical Data": [[9, "categorical-data"]], "Custom and Unusual Frequencies": [[9, "custom-and-unusual-frequencies"]], "Using the Transformers independently": [[9, "using-the-transformers-independently"]], "Note on ~Regression Models": [[9, "note-on-regression-models"]], "Models": [[9, "id9"]]}, "indexentries": {"anomalydetector (class in autots)": [[1, "autots.AnomalyDetector"]], "autots (class in autots)": [[1, "autots.AutoTS"]], "cassandra (class in autots)": [[1, "autots.Cassandra"]], "eventriskforecast (class in autots)": [[1, "autots.EventRiskForecast"]], "generaltransformer (class in autots)": [[1, "autots.GeneralTransformer"]], "holidaydetector (class in autots)": [[1, "autots.HolidayDetector"]], "randomtransform() (in module autots)": [[1, "autots.RandomTransform"]], "transformts (in module autots)": [[1, "autots.TransformTS"]], "analyze_trend() (autots.cassandra method)": [[1, "autots.Cassandra.analyze_trend"]], "anomalies (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.anomalies"]], "auto_fit() (autots.cassandra method)": [[1, "autots.Cassandra.auto_fit"]], "autots": [[1, "module-autots"]], "back_forecast() (autots.autots method)": [[1, "autots.AutoTS.back_forecast"]], "base_scaler() (autots.cassandra method)": [[1, "autots.Cassandra.base_scaler"]], "best_model (autots.autots attribute)": [[1, "autots.AutoTS.best_model"]], "best_model_ensemble (autots.autots attribute)": [[1, "autots.AutoTS.best_model_ensemble"]], "best_model_name (autots.autots attribute)": [[1, "autots.AutoTS.best_model_name"]], "best_model_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_params"]], "best_model_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_score"]], "best_model_transformation_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_transformation_params"]], "compare_actual_components() (autots.cassandra method)": [[1, "autots.Cassandra.compare_actual_components"]], "create_forecast_index() (autots.cassandra method)": [[1, "autots.Cassandra.create_forecast_index"]], "create_lagged_regressor() (in module autots)": [[1, "autots.create_lagged_regressor"]], "create_regressor() (in module autots)": [[1, "autots.create_regressor"]], "create_t() (autots.cassandra method)": [[1, "autots.Cassandra.create_t"]], "cross_validate() (autots.cassandra method)": [[1, "autots.Cassandra.cross_validate"]], "dates_to_holidays() (autots.cassandra.holiday_detector method)": [[1, "autots.Cassandra.holiday_detector.dates_to_holidays"]], "dates_to_holidays() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.dates_to_holidays"]], "detect() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.detect"]], "detect() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.detect"]], "df_wide_numeric (autots.autots attribute)": [[1, "autots.AutoTS.df_wide_numeric"]], "export_template() (autots.autots method)": [[1, "autots.AutoTS.export_template"]], "failure_rate() (autots.autots method)": [[1, "autots.AutoTS.failure_rate"]], "feature_importance() (autots.cassandra method)": [[1, "autots.Cassandra.feature_importance"]], "fill_na() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fill_na"]], "fit() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit"]], "fit() (autots.autots method)": [[1, "autots.AutoTS.fit"]], "fit() (autots.cassandra method)": [[1, "autots.Cassandra.fit"], [1, "id0"]], "fit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.fit"], [1, "id9"]], "fit() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit"]], "fit() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.fit"]], "fit_anomaly_classifier() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit_anomaly_classifier"]], "fit_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit_transform"]], "generate_historic_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_historic_risk_array"]], "generate_historic_risk_array() (autots.eventriskforecast static method)": [[1, "id10"]], "generate_result_windows() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_result_windows"], [1, "id11"]], "generate_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_risk_array"]], "generate_risk_array() (autots.eventriskforecast static method)": [[1, "id12"]], "get_new_params() (autots.anomalydetector static method)": [[1, "autots.AnomalyDetector.get_new_params"]], "get_new_params() (autots.autots static method)": [[1, "autots.AutoTS.get_new_params"]], "get_new_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_new_params"], [1, "id1"]], "get_new_params() (autots.generaltransformer static method)": [[1, "autots.GeneralTransformer.get_new_params"]], "get_new_params() (autots.holidaydetector static method)": [[1, "autots.HolidayDetector.get_new_params"]], "get_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_params"]], "holiday_count (autots.cassandra. attribute)": [[1, "autots.Cassandra..holiday_count"]], "holidays (autots.cassandra. attribute)": [[1, "autots.Cassandra..holidays"]], "horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.horizontal_per_generation"]], "horizontal_to_df() (autots.autots method)": [[1, "autots.AutoTS.horizontal_to_df"]], "import_results() (autots.autots method)": [[1, "autots.AutoTS.import_results"]], "import_template() (autots.autots method)": [[1, "autots.AutoTS.import_template"]], "inverse_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.inverse_transform"]], "list_failed_model_types() (autots.autots method)": [[1, "autots.AutoTS.list_failed_model_types"]], "load_artificial() (in module autots)": [[1, "autots.load_artificial"]], "load_daily() (in module autots)": [[1, "autots.load_daily"]], "load_hourly() (in module autots)": [[1, "autots.load_hourly"]], "load_linear() (in module autots)": [[1, "autots.load_linear"]], "load_live_daily() (in module autots)": [[1, "autots.load_live_daily"]], "load_monthly() (in module autots)": [[1, "autots.load_monthly"]], "load_sine() (in module autots)": [[1, "autots.load_sine"]], "load_weekdays() (in module autots)": [[1, "autots.load_weekdays"]], "load_weekly() (in module autots)": [[1, "autots.load_weekly"]], "load_yearly() (in module autots)": [[1, "autots.load_yearly"]], "long_to_wide() (in module autots)": [[1, "autots.long_to_wide"]], "model_forecast() (in module autots)": [[1, "autots.model_forecast"]], "model_results (autots.autots.initial_results attribute)": [[1, "autots.AutoTS.initial_results.model_results"]], "module": [[1, "module-autots"], [2, "module-autots.datasets"], [2, "module-autots.datasets.fred"], [3, 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(autots.anomalydetector method)": [[1, "autots.AnomalyDetector.plot"]], "plot() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot"], [1, "id13"]], "plot() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot"]], "plot_anomaly() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.autots method)": [[1, "autots.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.autots method)": [[1, "autots.AutoTS.plot_backforecast"]], "plot_components() (autots.cassandra method)": [[1, "autots.Cassandra.plot_components"], [1, "id2"]], "plot_eval() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot_eval"]], "plot_forecast() (autots.cassandra method)": [[1, "autots.Cassandra.plot_forecast"], [1, "id3"]], "plot_generation_loss() (autots.autots method)": [[1, "autots.AutoTS.plot_generation_loss"]], "plot_horizontal() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal"]], "plot_horizontal_model_count() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_transformers"]], "plot_per_series_error() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_mape"]], "plot_things() (autots.cassandra method)": [[1, "autots.Cassandra.plot_things"]], "plot_trend() (autots.cassandra method)": [[1, "autots.Cassandra.plot_trend"], [1, "id4"]], "plot_validations() (autots.autots method)": [[1, "autots.AutoTS.plot_validations"]], "predict() (autots.autots method)": [[1, "autots.AutoTS.predict"]], "predict() (autots.cassandra method)": [[1, "autots.Cassandra.predict"], [1, "id5"]], "predict() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict"], [1, "id14"]], "predict_historic() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict_historic"], [1, "id15"]], "predict_new_product() (autots.cassandra method)": [[1, "autots.Cassandra.predict_new_product"]], "predict_x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..predict_x_array"]], "predicted_trend (autots.cassandra. attribute)": [[1, "autots.Cassandra..predicted_trend"]], "process_components() (autots.cassandra method)": [[1, "autots.Cassandra.process_components"]], "regression_check (autots.autots attribute)": [[1, "autots.AutoTS.regression_check"]], "results() (autots.autots method)": [[1, "autots.AutoTS.results"]], "retrieve_transformer() (autots.generaltransformer class method)": [[1, "autots.GeneralTransformer.retrieve_transformer"]], "return_components() (autots.cassandra method)": [[1, "autots.Cassandra.return_components"], [1, "id6"]], "rolling_trend() (autots.cassandra method)": [[1, "autots.Cassandra.rolling_trend"]], "scale_data() (autots.cassandra method)": [[1, "autots.Cassandra.scale_data"]], "score_per_series (autots.autots attribute)": [[1, "autots.AutoTS.score_per_series"]], "score_to_anomaly() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.score_to_anomaly"]], "scores (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.scores"]], "set_limit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.set_limit"]], "set_limit() (autots.eventriskforecast static method)": [[1, "id16"]], "to_origin_space() (autots.cassandra method)": [[1, "autots.Cassandra.to_origin_space"]], "transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.transform"]], "treatment_causal_impact() (autots.cassandra method)": [[1, "autots.Cassandra.treatment_causal_impact"]], "trend_train (autots.cassandra. attribute)": [[1, "autots.Cassandra..trend_train"]], "x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..x_array"]], "autots.datasets": [[2, "module-autots.datasets"]], "autots.datasets.fred": [[2, "module-autots.datasets.fred"]], "get_fred_data() (in module autots.datasets.fred)": [[2, "autots.datasets.fred.get_fred_data"]], "load_artificial() (in module autots.datasets)": [[2, "autots.datasets.load_artificial"]], "load_daily() (in module autots.datasets)": [[2, "autots.datasets.load_daily"]], "load_hourly() (in module autots.datasets)": [[2, "autots.datasets.load_hourly"]], "load_linear() (in module autots.datasets)": [[2, "autots.datasets.load_linear"]], "load_live_daily() (in module autots.datasets)": [[2, "autots.datasets.load_live_daily"]], "load_monthly() (in module autots.datasets)": [[2, "autots.datasets.load_monthly"]], "load_sine() (in module autots.datasets)": [[2, "autots.datasets.load_sine"]], "load_weekdays() (in module autots.datasets)": [[2, "autots.datasets.load_weekdays"]], "load_weekly() (in module autots.datasets)": [[2, "autots.datasets.load_weekly"]], "load_yearly() (in module autots.datasets)": [[2, "autots.datasets.load_yearly"]], "load_zeroes() (in module autots.datasets)": [[2, "autots.datasets.load_zeroes"]], "anomalydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector"]], "autots (class in autots.evaluator.auto_ts)": [[3, "autots.evaluator.auto_ts.AutoTS"]], "autotsintervals (class in autots.evaluator.auto_ts)": [[3, "autots.evaluator.auto_ts.AutoTSIntervals"]], "benchmark (class in autots.evaluator.benchmark)": [[3, "autots.evaluator.benchmark.Benchmark"]], "eventriskforecast (class in autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast"]], "holidaydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector"]], "modelmonster() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelMonster"]], "modelprediction() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelPrediction"]], "newgenetictemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.NewGeneticTemplate"]], "randomtemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.RandomTemplate"]], "templateevalobject (class in autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateEvalObject"]], "templatewizard() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateWizard"]], "uniquetemplates() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.UniqueTemplates"]], "autots.evaluator": [[3, "module-autots.evaluator"]], "autots.evaluator.anomaly_detector": [[3, "module-autots.evaluator.anomaly_detector"]], "autots.evaluator.auto_model": [[3, "module-autots.evaluator.auto_model"]], "autots.evaluator.auto_ts": [[3, "module-autots.evaluator.auto_ts"]], "autots.evaluator.benchmark": [[3, 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"best_model_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_per_series_score"]], "best_model_transformation_params (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_transformation_params"]], "concat() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.concat"]], "containment() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.containment"]], "contour() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.contour"]], "create_model_id() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.create_model_id"]], "dates_to_holidays() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, 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"trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.MAR"]], "mlensemble (class in autots.models.mlensemble)": [[4, "autots.models.mlensemble.MLEnsemble"]], "metricmotif (class in autots.models.basics)": [[4, "autots.models.basics.MetricMotif"]], "modelobject (class in autots.models.base)": [[4, "autots.models.base.ModelObject"]], "mosaicensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.MosaicEnsemble"]], "motif (class in autots.models.basics)": [[4, "autots.models.basics.Motif"]], "motifsimulation (class in autots.models.basics)": [[4, "autots.models.basics.MotifSimulation"]], "multivariateregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.MultivariateRegression"]], "nvar (class in autots.models.basics)": [[4, 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"module-autots.tools.impute"]], "autots.tools.lunar module": [[6, "module-autots.tools.lunar"]], "autots.tools.percentile module": [[6, "module-autots.tools.percentile"]], "autots.tools.probabilistic module": [[6, "module-autots.tools.probabilistic"]], "autots.tools.profile module": [[6, "module-autots.tools.profile"]], "autots.tools.regressor module": [[6, "module-autots.tools.regressor"]], "autots.tools.seasonal module": [[6, "module-autots.tools.seasonal"]], "autots.tools.shaping module": [[6, "module-autots.tools.shaping"]], "autots.tools.thresholding module": [[6, "module-autots.tools.thresholding"]], "autots.tools.transform module": [[6, "module-autots.tools.transform"]], "autots.tools.window_functions module": [[6, "module-autots.tools.window_functions"]], "Intro": [[7, "intro"]], "Table of Contents": [[7, "table-of-contents"], [9, "table-of-contents"]], "Basic Use": [[7, "id2"]], "Tips for Speed and Large Data:": [[7, "id3"]], "How to Contribute:": [[7, "how-to-contribute"]], "autots": [[8, "autots"]], "Tutorial": [[9, "tutorial"]], "Extended Tutorial": [[9, "extended-tutorial"]], "A simple example": [[9, "id1"]], "Import of data": [[9, "import-of-data"]], "You can tailor the process in a few ways\u2026": [[9, "you-can-tailor-the-process-in-a-few-ways"]], "What to Worry About": [[9, "what-to-worry-about"]], "Validation and Cross Validation": [[9, "id2"]], "Another Example:": [[9, "id3"]], "Model Lists": [[9, "id4"]], "Deployment and Template Import/Export": [[9, "deployment-and-template-import-export"]], "Running Just One Model": [[9, "id5"]], "Metrics": [[9, "id6"]], "Hierarchial and Grouped Forecasts": [[9, "hierarchial-and-grouped-forecasts"]], "Ensembles": [[9, "id7"]], "Installation and Dependency Versioning": [[9, "installation-and-dependency-versioning"]], "Requirements:": [[9, "requirements"]], "Optional Packages": [[9, "optional-packages"]], "Safest bet for installation:": [[9, "safest-bet-for-installation"]], "Intel conda channel installation (sometime faster, also, more prone to bugs)": [[9, "intel-conda-channel-installation-sometime-faster-also-more-prone-to-bugs"]], "Speed Benchmark": [[9, "speed-benchmark"]], "Caveats and Advice": [[9, "caveats-and-advice"]], "Mysterious crashes": [[9, "mysterious-crashes"]], "Series IDs really need to be unique (or column names need to be all unique in wide data)": [[9, "series-ids-really-need-to-be-unique-or-column-names-need-to-be-all-unique-in-wide-data"]], "Short Training History": [[9, "short-training-history"]], "Adding regressors and other information": [[9, "adding-regressors-and-other-information"]], "Simulation Forecasting": [[9, "id8"]], "Event Risk Forecasting and Anomaly Detection": [[9, "event-risk-forecasting-and-anomaly-detection"]], "A Hack for Passing in Parameters (that aren\u2019t otherwise available)": [[9, "a-hack-for-passing-in-parameters-that-aren-t-otherwise-available"]], "Categorical Data": [[9, "categorical-data"]], "Custom and Unusual Frequencies": [[9, "custom-and-unusual-frequencies"]], "Using the Transformers independently": [[9, "using-the-transformers-independently"]], "Note on ~Regression Models": [[9, "note-on-regression-models"]], "Models": [[9, "id9"]]}, "indexentries": {"anomalydetector (class in autots)": [[1, "autots.AnomalyDetector"]], "autots (class in autots)": [[1, "autots.AutoTS"]], "cassandra (class in autots)": [[1, "autots.Cassandra"]], "eventriskforecast (class in autots)": [[1, "autots.EventRiskForecast"]], "generaltransformer (class in autots)": [[1, "autots.GeneralTransformer"]], "holidaydetector (class in autots)": [[1, "autots.HolidayDetector"]], "randomtransform() (in module autots)": [[1, "autots.RandomTransform"]], "transformts (in module autots)": [[1, "autots.TransformTS"]], "analyze_trend() (autots.cassandra method)": [[1, "autots.Cassandra.analyze_trend"]], "anomalies (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.anomalies"]], "auto_fit() (autots.cassandra method)": [[1, "autots.Cassandra.auto_fit"]], "autots": [[1, "module-autots"]], "back_forecast() (autots.autots method)": [[1, "autots.AutoTS.back_forecast"]], "base_scaler() (autots.cassandra method)": [[1, "autots.Cassandra.base_scaler"]], "best_model (autots.autots attribute)": [[1, "autots.AutoTS.best_model"]], "best_model_ensemble (autots.autots attribute)": [[1, "autots.AutoTS.best_model_ensemble"]], "best_model_name (autots.autots attribute)": [[1, "autots.AutoTS.best_model_name"]], "best_model_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_params"]], "best_model_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.autots method)": [[1, "autots.AutoTS.best_model_per_series_score"]], "best_model_transformation_params (autots.autots attribute)": [[1, "autots.AutoTS.best_model_transformation_params"]], "compare_actual_components() (autots.cassandra method)": [[1, "autots.Cassandra.compare_actual_components"]], "create_forecast_index() (autots.cassandra method)": [[1, "autots.Cassandra.create_forecast_index"]], "create_lagged_regressor() (in module autots)": [[1, "autots.create_lagged_regressor"]], "create_regressor() (in module autots)": [[1, "autots.create_regressor"]], "create_t() (autots.cassandra method)": [[1, "autots.Cassandra.create_t"]], "cross_validate() (autots.cassandra method)": [[1, "autots.Cassandra.cross_validate"]], "dates_to_holidays() (autots.cassandra.holiday_detector method)": [[1, "autots.Cassandra.holiday_detector.dates_to_holidays"]], "dates_to_holidays() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.dates_to_holidays"]], "detect() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.detect"]], "detect() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.detect"]], "df_wide_numeric (autots.autots attribute)": [[1, "autots.AutoTS.df_wide_numeric"]], "export_best_model() (autots.autots method)": [[1, "autots.AutoTS.export_best_model"]], "export_template() (autots.autots method)": [[1, "autots.AutoTS.export_template"]], "failure_rate() (autots.autots method)": [[1, "autots.AutoTS.failure_rate"]], "feature_importance() (autots.cassandra method)": [[1, "autots.Cassandra.feature_importance"]], "fill_na() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fill_na"]], "fit() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit"]], "fit() (autots.autots method)": [[1, "autots.AutoTS.fit"]], "fit() (autots.cassandra method)": [[1, "autots.Cassandra.fit"], [1, "id0"]], "fit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.fit"], [1, "id9"]], "fit() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit"]], "fit() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.fit"]], "fit_anomaly_classifier() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.fit_anomaly_classifier"]], "fit_data() (autots.autots method)": [[1, "autots.AutoTS.fit_data"]], "fit_data() (autots.cassandra method)": [[1, "autots.Cassandra.fit_data"]], "fit_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.fit_transform"]], "generate_historic_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_historic_risk_array"]], "generate_historic_risk_array() (autots.eventriskforecast static method)": [[1, "id10"]], "generate_result_windows() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_result_windows"], [1, "id11"]], "generate_risk_array() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.generate_risk_array"]], "generate_risk_array() (autots.eventriskforecast static method)": [[1, "id12"]], "get_new_params() (autots.anomalydetector static method)": [[1, "autots.AnomalyDetector.get_new_params"]], "get_new_params() (autots.autots static method)": [[1, "autots.AutoTS.get_new_params"]], "get_new_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_new_params"], [1, "id1"]], "get_new_params() (autots.generaltransformer static method)": [[1, "autots.GeneralTransformer.get_new_params"]], "get_new_params() (autots.holidaydetector static method)": [[1, "autots.HolidayDetector.get_new_params"]], "get_params() (autots.cassandra method)": [[1, "autots.Cassandra.get_params"]], "holiday_count (autots.cassandra. attribute)": [[1, "autots.Cassandra..holiday_count"]], "holidays (autots.cassandra. attribute)": [[1, "autots.Cassandra..holidays"]], "horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.horizontal_per_generation"]], "horizontal_to_df() (autots.autots method)": [[1, "autots.AutoTS.horizontal_to_df"]], "import_best_model() (autots.autots method)": [[1, "autots.AutoTS.import_best_model"]], "import_results() (autots.autots method)": [[1, "autots.AutoTS.import_results"]], "import_template() (autots.autots method)": [[1, "autots.AutoTS.import_template"]], "inverse_transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.inverse_transform"]], "list_failed_model_types() (autots.autots method)": [[1, "autots.AutoTS.list_failed_model_types"]], "load_artificial() (in module autots)": [[1, "autots.load_artificial"]], "load_daily() (in module autots)": [[1, "autots.load_daily"]], "load_hourly() (in module autots)": [[1, "autots.load_hourly"]], "load_linear() (in module autots)": [[1, "autots.load_linear"]], "load_live_daily() (in module autots)": [[1, "autots.load_live_daily"]], "load_monthly() (in module autots)": [[1, "autots.load_monthly"]], "load_sine() (in module autots)": [[1, "autots.load_sine"]], "load_template() (autots.autots method)": [[1, "autots.AutoTS.load_template"]], "load_weekdays() (in module autots)": [[1, "autots.load_weekdays"]], "load_weekly() (in module autots)": [[1, "autots.load_weekly"]], "load_yearly() (in module autots)": [[1, "autots.load_yearly"]], "long_to_wide() (in module autots)": [[1, "autots.long_to_wide"]], "model_forecast() (in module autots)": [[1, "autots.model_forecast"]], "model_results (autots.autots.initial_results attribute)": [[1, "autots.AutoTS.initial_results.model_results"]], "module": [[1, "module-autots"], [2, "module-autots.datasets"], [2, "module-autots.datasets.fred"], [3, "module-autots.evaluator"], [3, "module-autots.evaluator.anomaly_detector"], [3, "module-autots.evaluator.auto_model"], [3, "module-autots.evaluator.auto_ts"], [3, "module-autots.evaluator.benchmark"], [3, "module-autots.evaluator.event_forecasting"], [3, "module-autots.evaluator.metrics"], [3, "module-autots.evaluator.validation"], [4, "module-autots.models"], [4, "module-autots.models.arch"], [4, "module-autots.models.base"], [4, "module-autots.models.basics"], [4, "module-autots.models.cassandra"], [4, "module-autots.models.dnn"], [4, "module-autots.models.ensemble"], [4, "module-autots.models.gluonts"], [4, "module-autots.models.greykite"], [4, "module-autots.models.matrix_var"], [4, "module-autots.models.mlensemble"], [4, "module-autots.models.model_list"], [4, "module-autots.models.prophet"], [4, "module-autots.models.pytorch"], [4, "module-autots.models.sklearn"], [4, "module-autots.models.statsmodels"], [4, "module-autots.models.tfp"], [5, "module-autots.templates"], [5, "module-autots.templates.general"], [6, "module-autots.tools"], [6, "module-autots.tools.anomaly_utils"], [6, "module-autots.tools.calendar"], [6, "module-autots.tools.cointegration"], [6, "module-autots.tools.cpu_count"], [6, "module-autots.tools.fast_kalman"], [6, "module-autots.tools.hierarchial"], [6, "module-autots.tools.holiday"], [6, "module-autots.tools.impute"], [6, "module-autots.tools.lunar"], [6, "module-autots.tools.percentile"], [6, "module-autots.tools.probabilistic"], [6, "module-autots.tools.profile"], [6, "module-autots.tools.regressor"], [6, "module-autots.tools.seasonal"], [6, "module-autots.tools.shaping"], [6, "module-autots.tools.thresholding"], [6, "module-autots.tools.transform"], [6, "module-autots.tools.window_functions"]], "mosaic_to_df() (autots.autots method)": [[1, "autots.AutoTS.mosaic_to_df"]], "next_fit() (autots.cassandra method)": [[1, "autots.Cassandra.next_fit"]], "params (autots.cassandra. attribute)": [[1, "autots.Cassandra..params"]], "parse_best_model() (autots.autots method)": [[1, "autots.AutoTS.parse_best_model"]], "plot() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.plot"]], "plot() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot"], [1, "id13"]], "plot() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot"]], "plot_anomaly() (autots.holidaydetector method)": [[1, "autots.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.autots method)": [[1, "autots.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.autots method)": [[1, "autots.AutoTS.plot_backforecast"]], "plot_components() (autots.cassandra method)": [[1, "autots.Cassandra.plot_components"], [1, "id2"]], "plot_eval() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.plot_eval"]], "plot_forecast() (autots.cassandra method)": [[1, "autots.Cassandra.plot_forecast"], [1, "id3"]], "plot_generation_loss() (autots.autots method)": [[1, "autots.AutoTS.plot_generation_loss"]], "plot_horizontal() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal"]], "plot_horizontal_model_count() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_model_count"]], "plot_horizontal_per_generation() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_per_generation"]], "plot_horizontal_transformers() (autots.autots method)": [[1, "autots.AutoTS.plot_horizontal_transformers"]], "plot_per_series_error() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_error"]], "plot_per_series_mape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_mape"]], "plot_per_series_smape() (autots.autots method)": [[1, "autots.AutoTS.plot_per_series_smape"]], "plot_things() (autots.cassandra method)": [[1, "autots.Cassandra.plot_things"]], "plot_trend() (autots.cassandra method)": [[1, "autots.Cassandra.plot_trend"], [1, "id4"]], "plot_validations() (autots.autots method)": [[1, "autots.AutoTS.plot_validations"]], "predict() (autots.autots method)": [[1, "autots.AutoTS.predict"]], "predict() (autots.cassandra method)": [[1, "autots.Cassandra.predict"], [1, "id5"]], "predict() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict"], [1, "id14"]], "predict_historic() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.predict_historic"], [1, "id15"]], "predict_new_product() (autots.cassandra method)": [[1, "autots.Cassandra.predict_new_product"]], "predict_x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..predict_x_array"]], "predicted_trend (autots.cassandra. attribute)": [[1, "autots.Cassandra..predicted_trend"]], "process_components() (autots.cassandra method)": [[1, "autots.Cassandra.process_components"]], "regression_check (autots.autots attribute)": [[1, "autots.AutoTS.regression_check"]], "results() (autots.autots method)": [[1, "autots.AutoTS.results"]], "retrieve_transformer() (autots.generaltransformer class method)": [[1, "autots.GeneralTransformer.retrieve_transformer"]], "return_components() (autots.cassandra method)": [[1, "autots.Cassandra.return_components"], [1, "id6"]], "rolling_trend() (autots.cassandra method)": [[1, "autots.Cassandra.rolling_trend"]], "save_template() (autots.autots method)": [[1, "autots.AutoTS.save_template"]], "scale_data() (autots.cassandra method)": [[1, "autots.Cassandra.scale_data"]], "score_per_series (autots.autots attribute)": [[1, "autots.AutoTS.score_per_series"]], "score_to_anomaly() (autots.anomalydetector method)": [[1, "autots.AnomalyDetector.score_to_anomaly"]], "scores (autots.cassandra..anomaly_detector attribute)": [[1, "autots.Cassandra..anomaly_detector.scores"]], "set_limit() (autots.eventriskforecast method)": [[1, "autots.EventRiskForecast.set_limit"]], "set_limit() (autots.eventriskforecast static method)": [[1, "id16"]], "to_origin_space() (autots.cassandra method)": [[1, "autots.Cassandra.to_origin_space"]], "transform() (autots.generaltransformer method)": [[1, "autots.GeneralTransformer.transform"]], "treatment_causal_impact() (autots.cassandra method)": [[1, "autots.Cassandra.treatment_causal_impact"]], "trend_train (autots.cassandra. attribute)": [[1, "autots.Cassandra..trend_train"]], "x_array (autots.cassandra. attribute)": [[1, "autots.Cassandra..x_array"]], "autots.datasets": [[2, "module-autots.datasets"]], "autots.datasets.fred": [[2, "module-autots.datasets.fred"]], "get_fred_data() (in module autots.datasets.fred)": [[2, "autots.datasets.fred.get_fred_data"]], "load_artificial() (in module autots.datasets)": [[2, "autots.datasets.load_artificial"]], "load_daily() (in module autots.datasets)": [[2, "autots.datasets.load_daily"]], "load_hourly() (in module autots.datasets)": [[2, "autots.datasets.load_hourly"]], "load_linear() (in module autots.datasets)": [[2, "autots.datasets.load_linear"]], "load_live_daily() (in module autots.datasets)": [[2, "autots.datasets.load_live_daily"]], "load_monthly() (in module autots.datasets)": [[2, "autots.datasets.load_monthly"]], "load_sine() (in module autots.datasets)": [[2, "autots.datasets.load_sine"]], "load_weekdays() (in module autots.datasets)": [[2, "autots.datasets.load_weekdays"]], "load_weekly() (in module autots.datasets)": [[2, "autots.datasets.load_weekly"]], "load_yearly() (in module autots.datasets)": [[2, "autots.datasets.load_yearly"]], "load_zeroes() (in module autots.datasets)": [[2, "autots.datasets.load_zeroes"]], "anomalydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector"]], "autots (class in autots.evaluator.auto_ts)": [[3, "autots.evaluator.auto_ts.AutoTS"]], "autotsintervals (class in autots.evaluator.auto_ts)": [[3, "autots.evaluator.auto_ts.AutoTSIntervals"]], "benchmark (class in autots.evaluator.benchmark)": [[3, "autots.evaluator.benchmark.Benchmark"]], "eventriskforecast (class in autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast"]], "holidaydetector (class in autots.evaluator.anomaly_detector)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector"]], "modelmonster() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelMonster"]], "modelprediction (class in autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.ModelPrediction"]], "newgenetictemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.NewGeneticTemplate"]], "randomtemplate() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.RandomTemplate"]], "templateevalobject (class in autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateEvalObject"]], "templatewizard() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.TemplateWizard"]], "uniquetemplates() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.UniqueTemplates"]], "autots.evaluator": [[3, "module-autots.evaluator"]], "autots.evaluator.anomaly_detector": [[3, "module-autots.evaluator.anomaly_detector"]], "autots.evaluator.auto_model": [[3, "module-autots.evaluator.auto_model"]], "autots.evaluator.auto_ts": [[3, "module-autots.evaluator.auto_ts"]], "autots.evaluator.benchmark": [[3, "module-autots.evaluator.benchmark"]], "autots.evaluator.event_forecasting": [[3, "module-autots.evaluator.event_forecasting"]], "autots.evaluator.metrics": [[3, "module-autots.evaluator.metrics"]], "autots.evaluator.validation": [[3, "module-autots.evaluator.validation"]], "back_forecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.back_forecast"]], "back_forecast() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.back_forecast"]], "best_model (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model"]], "best_model_ensemble (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_ensemble"]], "best_model_name (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_name"]], "best_model_params (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_params"]], "best_model_per_series_mape() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_per_series_mape"]], "best_model_per_series_score() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_per_series_score"]], "best_model_transformation_params (autots.evaluator.auto_ts.autots attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.best_model_transformation_params"]], "concat() (autots.evaluator.auto_model.templateevalobject method)": [[3, "autots.evaluator.auto_model.TemplateEvalObject.concat"]], "containment() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.containment"]], "contour() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.contour"]], "create_model_id() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.create_model_id"]], "dates_to_holidays() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.dates_to_holidays"]], "detect() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.detect"]], "detect() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.detect"]], 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"medae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.medae"]], "median_absolute_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.median_absolute_error"]], "mlvb() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mlvb"]], "model_forecast() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.model_forecast"]], "model_results (autots.evaluator.auto_ts.autots.initial_results attribute)": [[3, "autots.evaluator.auto_ts.AutoTS.initial_results.model_results"]], "mosaic_to_df() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.mosaic_to_df"]], "mqae() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.mqae"]], "msle() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.msle"]], "oda() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.oda"]], "parse_best_model() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.parse_best_model"]], "pinball_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.pinball_loss"]], "plot() (autots.evaluator.anomaly_detector.anomalydetector method)": [[3, "autots.evaluator.anomaly_detector.AnomalyDetector.plot"]], "plot() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.plot"]], "plot() (autots.evaluator.event_forecasting.eventriskforecast method)": [[3, "autots.evaluator.event_forecasting.EventRiskForecast.plot"], [3, "id6"]], "plot_anomaly() (autots.evaluator.anomaly_detector.holidaydetector method)": [[3, "autots.evaluator.anomaly_detector.HolidayDetector.plot_anomaly"]], "plot_back_forecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_back_forecast"]], "plot_backforecast() (autots.evaluator.auto_ts.autots method)": [[3, "autots.evaluator.auto_ts.AutoTS.plot_backforecast"]], "plot_eval() 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autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast"]], "set_limit_forecast_historic() (in module autots.evaluator.event_forecasting)": [[3, "autots.evaluator.event_forecasting.set_limit_forecast_historic"]], "smape() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smape"]], "smoothness() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.smoothness"]], "spl() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.spl"]], "symmetric_mean_absolute_percentage_error() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.symmetric_mean_absolute_percentage_error"]], "threshold_loss() (in module autots.evaluator.metrics)": [[3, "autots.evaluator.metrics.threshold_loss"]], "trans_dict_recomb() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.trans_dict_recomb"]], "unpack_ensemble_models() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.unpack_ensemble_models"]], "validate_num_validations() (in module autots.evaluator.validation)": [[3, "autots.evaluator.validation.validate_num_validations"]], "validation_aggregation() (in module autots.evaluator.auto_model)": [[3, "autots.evaluator.auto_model.validation_aggregation"]], "arch (class in autots.models.arch)": [[4, "autots.models.arch.ARCH"]], "ardl (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARDL"]], "arima (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ARIMA"]], "averagevaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.AverageValueNaive"]], "bestnensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.BestNEnsemble"]], "cassandra (class in autots.models.cassandra)": [[4, "autots.models.cassandra.Cassandra"]], "componentanalysis (class in autots.models.sklearn)": [[4, "autots.models.sklearn.ComponentAnalysis"]], "constantnaive (class in autots.models.basics)": [[4, "autots.models.basics.ConstantNaive"]], "datepartregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.DatepartRegression"]], "distensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.DistEnsemble"]], "dynamicfactor (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactor"]], "dynamicfactormq (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.DynamicFactorMQ"]], "ets (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.ETS"]], "ensembleforecast() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleForecast"]], "ensembletemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.EnsembleTemplateGenerator"]], "fbprophet (class in autots.models.prophet)": [[4, "autots.models.prophet.FBProphet"]], "glm (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLM"]], "gls (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.GLS"]], "gluonts (class in autots.models.gluonts)": [[4, "autots.models.gluonts.GluonTS"]], "greykite (class in autots.models.greykite)": [[4, "autots.models.greykite.Greykite"]], "hdistensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HDistEnsemble"]], "horizontalensemble() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalEnsemble"]], "horizontaltemplategenerator() (in module autots.models.ensemble)": [[4, "autots.models.ensemble.HorizontalTemplateGenerator"]], "kalmanstatespace (class in autots.models.basics)": [[4, "autots.models.basics.KalmanStateSpace"]], "kerasrnn (class in autots.models.dnn)": [[4, "autots.models.dnn.KerasRNN"]], "latc (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.LATC"]], "lastvaluenaive (class in autots.models.basics)": [[4, "autots.models.basics.LastValueNaive"]], "mar (class in autots.models.matrix_var)": [[4, 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autots.models.pytorch)": [[4, "autots.models.pytorch.PytorchForecasting"]], "rrvar (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.RRVAR"]], "rollingregression (class in autots.models.sklearn)": [[4, "autots.models.sklearn.RollingRegression"]], "seasonalnaive (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalNaive"]], "seasonalitymotif (class in autots.models.basics)": [[4, "autots.models.basics.SeasonalityMotif"]], "sectionalmotif (class in autots.models.basics)": [[4, "autots.models.basics.SectionalMotif"]], "tfpregression (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegression"]], "tfpregressor (class in autots.models.tfp)": [[4, "autots.models.tfp.TFPRegressor"]], "tmf (class in autots.models.matrix_var)": [[4, "autots.models.matrix_var.TMF"]], "tensorflowsts (class in autots.models.tfp)": [[4, "autots.models.tfp.TensorflowSTS"]], "theta (class in autots.models.statsmodels)": [[4, "autots.models.statsmodels.Theta"]], "transformer 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method)": [[6, "autots.tools.transform.Round.inverse_transform"]], "inverse_transform() (autots.tools.transform.scipyfilter method)": [[6, "autots.tools.transform.ScipyFilter.inverse_transform"]], "inverse_transform() (autots.tools.transform.seasonaldifference method)": [[6, "autots.tools.transform.SeasonalDifference.inverse_transform"]], "inverse_transform() (autots.tools.transform.sintrend method)": [[6, "autots.tools.transform.SinTrend.inverse_transform"]], "inverse_transform() (autots.tools.transform.slice method)": [[6, "autots.tools.transform.Slice.inverse_transform"]], "kepler() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.kepler"]], "lagmat() (in module autots.tools.cointegration)": [[6, "autots.tools.cointegration.lagmat"]], "last_window() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.last_window"]], "limits_to_anomalies() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.limits_to_anomalies"]], "long_to_wide() (in module autots.tools.shaping)": [[6, "autots.tools.shaping.long_to_wide"]], "loop_sk_outliers() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.loop_sk_outliers"]], "lunar_from_lunar() (in module autots.tools.calendar)": [[6, "autots.tools.calendar.lunar_from_lunar"]], "lunar_from_lunar_full() (in module autots.tools.calendar)": [[6, "autots.tools.calendar.lunar_from_lunar_full"]], "moon_phase() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.moon_phase"]], "moon_phase_df() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.moon_phase_df"]], "nan_percentile() (in module autots.tools.percentile)": [[6, "autots.tools.percentile.nan_percentile"]], "nan_quantile() (in module autots.tools.percentile)": [[6, "autots.tools.percentile.nan_quantile"]], "nonparametric() (in module autots.tools.thresholding)": [[6, "autots.tools.thresholding.nonparametric"]], "nonparametric_multivariate() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.nonparametric_multivariate"]], "percentileofscore_appliable() (in module autots.tools.probabilistic)": [[6, "autots.tools.probabilistic.percentileofscore_appliable"]], "phase_string() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.phase_string"]], "predict() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.predict"]], "predict() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.predict"]], "predict_next() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.predict_next"]], "predict_observation() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.predict_observation"]], "predict_observation() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.predict_observation"]], "priv_smooth() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.priv_smooth"]], "priv_update_with_nan_check() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.priv_update_with_nan_check"]], "prune_anoms() (autots.tools.thresholding.nonparametricthreshold method)": [[6, "autots.tools.thresholding.NonparametricThreshold.prune_anoms"]], "query_holidays() (in module autots.tools.holiday)": [[6, "autots.tools.holiday.query_holidays"]], "random_cleaners() (in module autots.tools.transform)": [[6, "autots.tools.transform.random_cleaners"]], "random_state_space() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.random_state_space"]], "reconcile() (autots.tools.hierarchial.hierarchial method)": [[6, "autots.tools.hierarchial.hierarchial.reconcile"]], "remove_outliers() (in module autots.tools.transform)": [[6, "autots.tools.transform.remove_outliers"]], "retrieve_closest_indices() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.retrieve_closest_indices"]], "retrieve_transformer() (autots.tools.transform.generaltransformer class method)": [[6, "autots.tools.transform.GeneralTransformer.retrieve_transformer"]], "rolling_mean() (in module autots.tools.impute)": [[6, "autots.tools.impute.rolling_mean"]], "rolling_window_view() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.rolling_window_view"]], "score_anomalies() (autots.tools.thresholding.nonparametricthreshold method)": [[6, "autots.tools.thresholding.NonparametricThreshold.score_anomalies"]], "score_to_anomaly() (autots.tools.transform.anomalyremoval method)": [[6, "autots.tools.transform.AnomalyRemoval.score_to_anomaly"]], "seasonal_independent_match() (in module autots.tools.seasonal)": [[6, "autots.tools.seasonal.seasonal_independent_match"]], "seasonal_int() (in module autots.tools.seasonal)": [[6, "autots.tools.seasonal.seasonal_int"]], "seasonal_window_match() (in module autots.tools.seasonal)": [[6, "autots.tools.seasonal.seasonal_window_match"]], "simple_context_slicer() (in module autots.tools.transform)": [[6, "autots.tools.transform.simple_context_slicer"]], "simple_train_test_split() (in module autots.tools.shaping)": [[6, "autots.tools.shaping.simple_train_test_split"]], "sk_outliers() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.sk_outliers"]], "sliding_window_view() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.sliding_window_view"]], "smooth() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.smooth"]], "smooth() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.smooth"]], "smooth_current() (autots.tools.fast_kalman.kalmanfilter method)": [[6, "autots.tools.fast_kalman.KalmanFilter.smooth_current"]], "subset_series() (in module autots.tools.shaping)": [[6, "autots.tools.shaping.subset_series"]], "to_jd() (in module autots.tools.calendar)": [[6, "autots.tools.calendar.to_jd"]], "todeg() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.todeg"]], "torad() (in module autots.tools.lunar)": [[6, "autots.tools.lunar.torad"]], "transform() (autots.tools.hierarchial.hierarchial method)": [[6, "autots.tools.hierarchial.hierarchial.transform"]], "transform() (autots.tools.shaping.numerictransformer method)": [[6, "autots.tools.shaping.NumericTransformer.transform"]], "transform() (autots.tools.transform.alignlastvalue method)": [[6, "autots.tools.transform.AlignLastValue.transform"]], "transform() (autots.tools.transform.anomalyremoval method)": [[6, "autots.tools.transform.AnomalyRemoval.transform"]], "transform() (autots.tools.transform.btcd method)": [[6, "autots.tools.transform.BTCD.transform"]], "transform() (autots.tools.transform.centerlastvalue method)": [[6, "autots.tools.transform.CenterLastValue.transform"]], "transform() (autots.tools.transform.clipoutliers method)": [[6, "autots.tools.transform.ClipOutliers.transform"]], "transform() (autots.tools.transform.cointegration method)": [[6, "autots.tools.transform.Cointegration.transform"]], "transform() (autots.tools.transform.cumsumtransformer method)": [[6, "autots.tools.transform.CumSumTransformer.transform"]], "transform() (autots.tools.transform.datepartregressiontransformer method)": [[6, "autots.tools.transform.DatepartRegressionTransformer.transform"]], "transform() (autots.tools.transform.detrend method)": [[6, "autots.tools.transform.Detrend.transform"]], "transform() (autots.tools.transform.differencedtransformer method)": [[6, "autots.tools.transform.DifferencedTransformer.transform"]], "transform() (autots.tools.transform.discretize method)": [[6, "autots.tools.transform.Discretize.transform"]], "transform() (autots.tools.transform.ewmafilter method)": [[6, "autots.tools.transform.EWMAFilter.transform"]], "transform() (autots.tools.transform.emptytransformer method)": [[6, "autots.tools.transform.EmptyTransformer.transform"]], "transform() (autots.tools.transform.fastica method)": [[6, "autots.tools.transform.FastICA.transform"]], "transform() (autots.tools.transform.generaltransformer method)": [[6, "autots.tools.transform.GeneralTransformer.transform"]], "transform() (autots.tools.transform.hpfilter method)": [[6, "autots.tools.transform.HPFilter.transform"]], "transform() (autots.tools.transform.holidaytransformer method)": [[6, "autots.tools.transform.HolidayTransformer.transform"]], "transform() (autots.tools.transform.intermittentoccurrence method)": [[6, "autots.tools.transform.IntermittentOccurrence.transform"]], "transform() (autots.tools.transform.kalmansmoothing method)": [[6, "autots.tools.transform.KalmanSmoothing.transform"]], "transform() (autots.tools.transform.levelshiftmagic method)": [[6, "autots.tools.transform.LevelShiftMagic.transform"]], "transform() (autots.tools.transform.locallineartrend method)": [[6, "autots.tools.transform.LocalLinearTrend.transform"]], "transform() (autots.tools.transform.meandifference method)": [[6, "autots.tools.transform.MeanDifference.transform"]], "transform() (autots.tools.transform.pca method)": [[6, "autots.tools.transform.PCA.transform"]], "transform() (autots.tools.transform.pctchangetransformer method)": [[6, "autots.tools.transform.PctChangeTransformer.transform"]], "transform() (autots.tools.transform.positiveshift method)": [[6, "autots.tools.transform.PositiveShift.transform"]], "transform() (autots.tools.transform.regressionfilter method)": [[6, "autots.tools.transform.RegressionFilter.transform"]], "transform() (autots.tools.transform.rollingmeantransformer method)": [[6, "autots.tools.transform.RollingMeanTransformer.transform"]], "transform() (autots.tools.transform.round method)": [[6, "autots.tools.transform.Round.transform"]], "transform() (autots.tools.transform.stlfilter method)": [[6, "autots.tools.transform.STLFilter.transform"]], "transform() (autots.tools.transform.scipyfilter method)": [[6, 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"autots.tools.fast_kalman.KalmanFilter.update"]], "update() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.update"]], "update_with_nan_check() (in module autots.tools.fast_kalman)": [[6, "autots.tools.fast_kalman.update_with_nan_check"]], "values_to_anomalies() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.values_to_anomalies"]], "wide_to_3d() (in module autots.tools.shaping)": [[6, "autots.tools.shaping.wide_to_3d"]], "window_id_maker() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_id_maker"]], "window_lin_reg() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg"]], "window_lin_reg_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_lin_reg_mean"]], "window_maker() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker"]], "window_maker_2() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_2"]], "window_maker_3() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_maker_3"]], "window_sum_nan_mean() (in module autots.tools.window_functions)": [[6, "autots.tools.window_functions.window_sum_nan_mean"]], "zscore_survival_function() (in module autots.tools.anomaly_utils)": [[6, "autots.tools.anomaly_utils.zscore_survival_function"]]}}) \ No newline at end of file diff --git a/docs/build/html/source/autots.datasets.html b/docs/build/html/source/autots.datasets.html index 361c62ea..493132a7 100644 --- a/docs/build/html/source/autots.datasets.html +++ b/docs/build/html/source/autots.datasets.html @@ -6,7 +6,7 @@ - autots.datasets package — AutoTS 0.5.8 documentation + autots.datasets package — AutoTS 0.6.0 documentation diff --git a/docs/build/html/source/autots.evaluator.html b/docs/build/html/source/autots.evaluator.html index cf145ab1..86223544 100644 --- a/docs/build/html/source/autots.evaluator.html +++ b/docs/build/html/source/autots.evaluator.html @@ -6,7 +6,7 @@ - autots.evaluator package — AutoTS 0.5.8 documentation + autots.evaluator package — AutoTS 0.6.0 documentation @@ -138,7 +138,7 @@

      Submodules
      -plot(series_name=None, include_anomalies=True, title=None, plot_kwargs={})
      +plot(series_name=None, include_anomalies=True, title=None, plot_kwargs={}, series=None)
      @@ -166,10 +166,11 @@

      Submodules +
      -autots.evaluator.auto_model.ModelPrediction(df_train, forecast_length: int, transformation_dict: dict, model_str: str, parameter_dict: dict, frequency: str = 'infer', prediction_interval: float = 0.9, no_negatives: bool = False, constraint: float | None = None, future_regressor_train=None, future_regressor_forecast=None, holiday_country: str = 'US', startTimeStamps=None, grouping_ids=None, fail_on_forecast_nan: bool = True, return_model: bool = False, random_seed: int = 2020, verbose: int = 0, n_jobs: int | None = None, current_model_file: str | None = None, model_count: int = 0)
      -

      Feed parameters into modeling pipeline

      +class autots.evaluator.auto_model.ModelPrediction(forecast_length: int, transformation_dict: dict, model_str: str, parameter_dict: dict, frequency: str = 'infer', prediction_interval: float = 0.9, no_negatives: bool = False, constraint: float | None = None, holiday_country: str = 'US', startTimeStamps=None, grouping_ids=None, fail_on_forecast_nan: bool = True, return_model: bool = False, random_seed: int = 2020, verbose: int = 0, n_jobs: int | None = None, current_model_file: str | None = None, model_count: int = 0) +

      Bases: ModelObject

      +

      Feed parameters into modeling pipeline. A class object, does NOT work with ensembles.

      Parameters:
      +
      +
      +fit(df, future_regressor=None)
      +
      + +
      +
      +fit_data(df, future_regressor=None)
      +
      + +
      +
      +predict(forecast_length=None, future_regressor=None)
      +
      +
      @@ -465,7 +481,7 @@

      Submodules
      -class autots.evaluator.auto_ts.AutoTS(forecast_length: int = 14, frequency: str = 'infer', prediction_interval: float = 0.9, max_generations: int = 10, no_negatives: bool = False, constraint: float | None = None, ensemble: str = 'auto', initial_template: str = 'General+Random', random_seed: int = 2022, holiday_country: str = 'US', subset: int | None = None, aggfunc: str = 'first', na_tolerance: float = 1, metric_weighting: dict = {'containment_weighting': 0, 'contour_weighting': 1, 'imle_weighting': 0, 'made_weighting': 0.5, 'mae_weighting': 2, 'mage_weighting': 0, 'mle_weighting': 0, 'oda_weighting': 0.001, 'rmse_weighting': 2, 'runtime_weighting': 0.05, 'smape_weighting': 5, 'spl_weighting': 3}, drop_most_recent: int = 0, drop_data_older_than_periods: int = 100000, model_list: str = 'default', transformer_list: dict = 'auto', transformer_max_depth: int = 6, models_mode: str = 'random', num_validations: int = 'auto', models_to_validate: float = 0.15, max_per_model_class: int | None = None, validation_method: str = 'backwards', min_allowed_train_percent: float = 0.5, remove_leading_zeroes: bool = False, prefill_na: str | None = None, introduce_na: bool | None = None, preclean: dict | None = None, model_interrupt: bool = True, generation_timeout: int | None = None, current_model_file: str | None = None, verbose: int = 1, n_jobs: int = -2)
      +class autots.evaluator.auto_ts.AutoTS(forecast_length: int = 14, frequency: str = 'infer', prediction_interval: float = 0.9, max_generations: int = 10, no_negatives: bool = False, constraint: float | None = None, ensemble: str | None = None, initial_template: str = 'General+Random', random_seed: int = 2022, holiday_country: str = 'US', subset: int | None = None, aggfunc: str = 'first', na_tolerance: float = 1, metric_weighting: dict = {'containment_weighting': 0, 'contour_weighting': 1, 'imle_weighting': 0, 'made_weighting': 0.5, 'mae_weighting': 2, 'mage_weighting': 0, 'mle_weighting': 0, 'oda_weighting': 0.001, 'rmse_weighting': 2, 'runtime_weighting': 0.05, 'smape_weighting': 5, 'spl_weighting': 3}, drop_most_recent: int = 0, drop_data_older_than_periods: int | None = None, model_list: str = 'default', transformer_list: dict = 'auto', transformer_max_depth: int = 6, models_mode: str = 'random', num_validations: int = 'auto', models_to_validate: float = 0.15, max_per_model_class: int | None = None, validation_method: str = 'backwards', min_allowed_train_percent: float = 0.5, remove_leading_zeroes: bool = False, prefill_na: str | None = None, introduce_na: bool | None = None, preclean: dict | None = None, model_interrupt: bool = True, generation_timeout: int | None = None, current_model_file: str | None = None, verbose: int = 1, n_jobs: int = -2)

      Bases: object

      Automate time series modeling using a genetic algorithm.

      @@ -504,7 +520,8 @@

      Submodulesbest_model_per_series_score()

      +
      +
      +export_best_model(filename, **kwargs)
      +

      Basically the same as export_template but only ever the one best model.

      +
      +
      export_template(filename=None, models: str = 'best', n: int = 20, max_per_model_class: int | None = None, include_results: bool = False)
      @@ -765,6 +788,12 @@

      Submodules +
      +fit_data(df, date_col=None, value_col=None, id_col=None, future_regressor=None, weights={})
      +

      Part of the setup that involves fitting the initial data but not running any models.

      +

      +
      static get_new_params(method='random')
      @@ -782,6 +811,17 @@

      Submodules +
      +import_best_model(import_target, enforce_model_list: bool = True, include_ensemble: bool = True)
      +

      Load a best model, overriding any existing setting.

      +
      +
      Parameters:
      +

      import_target – pd.DataFrame or file path

      +
      +
      +

      +
      import_results(filename)
      @@ -814,12 +854,23 @@

      Submodules +
      +load_template(filename)
      +

      Helper funciton for just loading the file part of import_template.

      +

      +
      mosaic_to_df()

      Helper function to create a readable df of models in mosaic.

      +
      +
      +parse_best_model()
      +
      +
      plot_back_forecast(**kwargs)
      @@ -936,9 +987,15 @@

      Submodules +
      +plot_per_series_smape(title: str | None = None, max_series: int = 10, max_name_chars: int = 25, color: str = '#ff9912', figsize=(12, 4), kind: str = 'bar', **kwargs)
      +

      To be backwards compatible, not necessarily maintained, plot_per_series_mape is to be preferred.

      +

      +
      -plot_validations(models=None, series=None, title=None, start_date='auto', end_date=None, subset=None, compare_horizontal=False, colors=None, include_bounds=True, alpha=0.35, **kwargs)
      +plot_validations(df_wide=None, models=None, series=None, title=None, start_date='auto', end_date=None, subset=None, compare_horizontal=False, colors=None, include_bounds=True, alpha=0.35, **kwargs)

      Similar to plot_backforecast but using the model’s validation segments specifically. Must reforecast. Saves results to self.validation_forecasts and caches. Set that to None to force rerun otherwise it uses stored (when models is the same). ‘chosen’ refers to best_model_id, the model chosen to run for predict

      @@ -962,6 +1019,20 @@

      Submodules predict(forecast_length: int = 'self', prediction_interval: float = 'self', future_regressor=None, hierarchy=None, just_point_forecast: bool = False, fail_on_forecast_nan: bool = True, verbose: int = 'self')

      Generate forecast data immediately following dates of index supplied to .fit().

      +

      If using a model from update_fit list, with no ensembling, underlying model will not be retrained when used as below, with a single prediction interval: +This designed for high speed forecasting. Full retraining is best when there is sufficient time. +`python +model = AutoTS(model_list='update_fit') +model.fit(df) +model.predict() +# for new data without retraining +model.fit_data(df) +model.predict() +# to force retrain of best model (but not full model search) +model.model = None +model.fit_data(df) +model.predict() +`

      Parameters:
      +

      diff --git a/docs/build/html/source/autots.html b/docs/build/html/source/autots.html index 38bdc6b3..91771ab2 100644 --- a/docs/build/html/source/autots.html +++ b/docs/build/html/source/autots.html @@ -6,7 +6,7 @@ - autots package — AutoTS 0.5.8 documentation + autots package — AutoTS 0.6.0 documentation @@ -85,7 +85,12 @@

      Subpackagesautots.evaluator.auto_model module

      +
      get_new_params(method: str = 'random')
      @@ -952,7 +957,7 @@

      Submodules
      -class autots.models.basics.SeasonalityMotif(name: str = 'SeasonalityMotif', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, regression_type: str | None = None, window: int = 5, point_method: str = 'mean', distance_metric: str = 'mae', k: int = 10, datepart_method: str = 'common_fourier', **kwargs)
      +class autots.models.basics.SeasonalityMotif(name: str = 'SeasonalityMotif', frequency: str = 'infer', prediction_interval: float = 0.9, holiday_country: str = 'US', random_seed: int = 2020, verbose: int = 0, regression_type: str | None = None, window: int = 5, point_method: str = 'mean', distance_metric: str = 'mae', k: int = 10, datepart_method: str = 'common_fourier', independent: bool = False, **kwargs)

      Bases: ModelObject

      Forecasts using a nearest neighbors type model adapted for probabilistic time series. This version is fully vectorized, using basic metrics for distance comparison.

      @@ -1310,6 +1315,11 @@

      Submodulesfit(df, future_regressor=None, regressor_per_series=None, flag_regressors=None, categorical_groups=None, past_impacts=None)

      +
      +
      +fit_data(df, forecast_length=None, future_regressor=None, regressor_per_series=None, flag_regressors=None, future_impacts=None, regressor_forecast_model=None, regressor_forecast_model_params=None, regressor_forecast_transformations=None, include_history=True, past_impacts=None)
      +
      +
      get_new_params(method='fast')
      @@ -1364,10 +1374,22 @@

      Submodules
      -predict(forecast_length, include_history=False, future_regressor=None, regressor_per_series=None, flag_regressors=None, future_impacts=None, new_df=None, regressor_forecast_model=None, regressor_forecast_model_params=None, regressor_forecast_transformations=None, include_organic=False)
      +predict(forecast_length=None, include_history=False, future_regressor=None, regressor_per_series=None, flag_regressors=None, future_impacts=None, new_df=None, regressor_forecast_model=None, regressor_forecast_model_params=None, regressor_forecast_transformations=None, include_organic=False, df=None, past_impacts=None)

      Generate a forecast.

      future_regressor and regressor_per_series should only include new future values, history is already stored they should match on forecast_length and index of forecasts

      +
      +
      Parameters:
      +
        +
      • forecast_length (int) – steps ahead to predict, or None

      • +
      • include_history (bool) – include past predictions if True

      • +
      • .fit (all the same regressor args as) –

      • +
      • here (but future forecast versions) –

      • +
      • future_impacts (pd.DataFrame) – like past impacts but for the forecast ahead

      • +
      • new_df (pd.DataFrame) – or df, equivalent to fit_data update

      • +
      +
      +

      @@ -1772,6 +1794,11 @@

      Submodules +
      +fit_data(df, future_regressor=None)
      +

      +
      get_new_params(method: str = 'random')
      @@ -1786,7 +1813,7 @@

      Submodules
      -predict(forecast_length: int, future_regressor=[], just_point_forecast=False)
      +predict(forecast_length: int | None = None, future_regressor=[], just_point_forecast=False)

      Generates forecast data immediately following dates of index supplied to .fit()

      Parameters:
      @@ -2554,7 +2581,7 @@

      Submodules
      -predict(forecast_length: int, future_regressor=None, just_point_forecast: bool = False)
      +predict(forecast_length: int | None = None, future_regressor=None, just_point_forecast: bool = False)

      Generate forecast data immediately following dates of .fit().

      Parameters:
      @@ -2598,6 +2625,11 @@

      Submodules +
      +fit_data(df, future_regressor=None)
      +

      +
      get_new_params(method: str = 'random')
      @@ -2612,7 +2644,7 @@

      Submodules
      -predict(forecast_length: int, future_regressor=None, just_point_forecast: bool = False)
      +predict(forecast_length: int | None = None, future_regressor=None, just_point_forecast: bool = False, df=None)

      Generate forecast data immediately following dates of index supplied to .fit().

      Parameters:
      @@ -2662,6 +2694,11 @@

      Submodules +
      +fit_data(df, future_regressor=None)
      +

      +
      get_new_params(method: str = 'random')
      @@ -2676,7 +2713,7 @@

      Submodules
      -predict(forecast_length: int | None = None, just_point_forecast: bool = False, future_regressor=None)
      +predict(forecast_length: int | None = None, just_point_forecast: bool = False, future_regressor=None, df=None)

      Generate forecast data immediately following dates of index supplied to .fit().

      Parameters:
      @@ -2852,6 +2889,11 @@

      Submodules +
      +fit_data(df, future_regressor=None)
      +

      +
      get_new_params(method: str = 'random')
      @@ -2866,7 +2908,7 @@

      Submodules
      -predict(forecast_length: int, future_regressor=None, just_point_forecast: bool = False)
      +predict(forecast_length: int | None = None, future_regressor=None, just_point_forecast: bool = False, df=None)

      Generate forecast data immediately following dates of .fit().

      Parameters:
      @@ -2892,7 +2934,7 @@

      Submodules
      -autots.models.sklearn.retrieve_regressor(regression_model: dict = {'model': 'Adaboost', 'model_params': {'estimator': 'DecisionTree', 'learning_rate': 1.0, 'loss': 'linear', 'n_estimators': 50}}, verbose: int = 0, verbose_bool: bool = False, random_seed: int = 2020, n_jobs: int = 1, multioutput: bool = True)
      +autots.models.sklearn.retrieve_regressor(regression_model: dict = {'model': 'RandomForest', 'model_params': {'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 300}}, verbose: int = 0, verbose_bool: bool = False, random_seed: int = 2020, n_jobs: int = 1, multioutput: bool = True)

      Convert a model param dict to model object for regression frameworks.

      diff --git a/docs/build/html/source/autots.templates.html b/docs/build/html/source/autots.templates.html index 9dffca3b..3e8037f7 100644 --- a/docs/build/html/source/autots.templates.html +++ b/docs/build/html/source/autots.templates.html @@ -6,7 +6,7 @@ - autots.templates package — AutoTS 0.5.8 documentation + autots.templates package — AutoTS 0.6.0 documentation @@ -42,7 +42,7 @@

      Submodules
      -autots.templates.general.general_template = Model  ... Ensemble 1                ARIMA  ...        0 3    AverageValueNaive  ...        0 4    AverageValueNaive  ...        0 5    AverageValueNaive  ...        0 6   DatepartRegression  ...        0 ..                 ...  ...      ... 63     MotifSimulation  ...        0 64       DynamicFactor  ...        0 65   RollingRegression  ...        0 66                ARCH  ...        0 67           Cassandra  ...        0  [66 rows x 4 columns]
      +autots.templates.general.general_template = Model                                    ModelParameters                           TransformationParameters  Ensemble 1                ARIMA  {"p": 4, "d": 0, "q": 12, "regression_type": n...  {"fillna": "cubic", "transformations": {"0": "...         0 3    AverageValueNaive                                 {"method": "Mean"}  {"fillna": "fake_date", "transformations": {"0...         0 4    AverageValueNaive                                 {"method": "Mean"}  {"fillna": "mean", "transformations": {"0": "C...         0 5    AverageValueNaive                                 {"method": "Mean"}  {"fillna": "rolling_mean_24", "transformations...         0 6   DatepartRegression  {"regression_model": {"model": "DecisionTree",...  {"fillna": "mean", "transformations": {"0": "C...         0 ..                 ...                                                ...                                                ...       ... 64       DynamicFactor  {"k_factors": 0, "factor_order": 0, "regressio...  {"fillna": "ffill", "transformations": {"0": "...         0 65   RollingRegression  {"regression_model": {"model": "ExtraTrees", "...  {"fillna": "ffill_mean_biased", "transformatio...         0 66                ARCH  {"mean": "Zero", "lags": 1, "vol": "GARCH", "p...  {"fillna": "ffill", "transformations": {"0": "...         0 67           Cassandra  {\n            "preprocessing_transformation":...  {"fillna": null, "transformations": {}, "trans...         0 68    SeasonalityMotif  {\n            "window": 5, "point_method": "w...  {"fillna": "nearest", "transformations": {"0":...         0  [67 rows x 4 columns]

      # Basic Template Construction Code # transformer_max_depth = 6 and transformer_list = “fast” from autots.evaluator.auto_model import unpack_ensemble_models @@ -82,24 +82,6 @@

      Submodules -
      -autots.templates.general.general_template_dict = {'1': {'Ensemble': 0, 'Model': 'ARIMA', 'ModelParameters': '{"p": 4, "d": 0, "q": 12, "regression_type": null}', 'TransformationParameters': '{"fillna": "cubic", "transformations": {"0": "bkfilter"}, "transformation_params": {"0": {}}}'}, '10': {'Ensemble': 0, 'Model': 'ETS', 'ModelParameters': '{"damped_trend": false, "trend": "additive", "seasonal": null, "seasonal_periods": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '11': {'Ensemble': 0, 'Model': 'ETS', 'ModelParameters': '{"damped_trend": false, "trend": null, "seasonal": "additive", "seasonal_periods": 7}', 'TransformationParameters': '{"fillna": "rolling_mean", "transformations": {"0": "ClipOutliers", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "middle", "decimals": 0, "on_transform": false, "on_inverse": true}, "2": {"model": "GLS"}}}'}, '12': {'Ensemble': 0, 'Model': 'GLM', 'ModelParameters': '{"family": "Binomial", "constant": false, "regression_type": "datepart"}', 'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 4, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '13': {'Ensemble': 0, 'Model': 'GLM', 'ModelParameters': '{"family": "Binomial", "constant": false, "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '14': {'Ensemble': 0, 'Model': 'GLS', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "rolling_mean", "transformations": {"0": "RollingMeanTransformer", "1": "DifferencedTransformer", "2": "Detrend", "3": "Slice"}, "transformation_params": {"0": {"fixed": true, "window": 3}, "1": {}, "2": {"model": "Linear"}, "3": {"method": 100}}}'}, '15': {'Ensemble': 0, 'Model': 'GLS', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "median", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler", "3": "Round", "4": "MaxAbsScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}, "3": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": true}, "4": {}}}'}, '16': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "DeepAR", "epochs": 150, "learning_rate": 0.001, "context_length": 10}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "DifferencedTransformer", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}'}, '17': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "NPTS", "epochs": 20, "learning_rate": 0.001, "context_length": 10}', 'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "Detrend", "1": "DifferencedTransformer"}, "transformation_params": {"0": {"model": "Linear"}, "1": {}}}'}, '18': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "WaveNet", "epochs": 40, "learning_rate": 0.001, "context_length": 10}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '19': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "Transformer", "epochs": 40, "learning_rate": 0.001, "context_length": 10}', 'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "QuantileTransformer", "1": "MaxAbsScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {}}}'}, '20': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "SFF", "epochs": 40, "learning_rate": 0.01, "context_length": 10}', 'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "QuantileTransformer"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '21': {'Ensemble': 0, 'Model': 'LastValueNaive', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "bkfilter", "1": "SinTrend", "2": "Detrend", "3": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {}, "2": {"model": "Linear"}, "3": {}}}'}, '22': {'Ensemble': 0, 'Model': 'LastValueNaive', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "PositiveShift", "1": "SinTrend", "2": "bkfilter"}, "transformation_params": {"0": {}, "1": {}, "2": {}}}'}, '23': {'Ensemble': 0, 'Model': 'LastValueNaive', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "SinTrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}}}'}, '24': {'Ensemble': 0, 'Model': 'LastValueNaive', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 1, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '25': {'Ensemble': 0, 'Model': 'SeasonalNaive', 'ModelParameters': '{"method": "LastValue", "lag_1": 2, "lag_2": 7}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "SinTrend", "1": "Round", "2": "PowerTransformer"}, "transformation_params": {"0": {}, "1": {"model": "middle", "decimals": 2, "on_transform": false, "on_inverse": true}, "2": {}}}'}, '26': {'Ensemble': 0, 'Model': 'SeasonalNaive', 'ModelParameters': '{"method": "LastValue", "lag_1": 2, "lag_2": 1}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 12, "method": "Median"}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "GLS"}}}'}, '27': {'Ensemble': 0, 'Model': 'SeasonalNaive', 'ModelParameters': '{"method": "LastValue", "lag_1": 7, "lag_2": 2}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "ClipOutliers"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"method": "clip", "std_threshold": 2, "fillna": null}}}'}, '28': {'Ensemble': 0, 'Model': 'UnobservedComponents', 'ModelParameters': '{"level": true, "trend": false, "cycle": true, "damped_cycle": true, "irregular": true, "stochastic_trend": false, "stochastic_level": true, "stochastic_cycle": true, "regression_type": "Holiday"}', 'TransformationParameters': '{"fillna": "fake_date", "transformations": {"0": "PositiveShift", "1": "Detrend", "2": "bkfilter", "3": "DifferencedTransformer"}, "transformation_params": {"0": {}, "1": {"model": "Linear"}, "2": {}, "3": {}}}'}, '29': {'Ensemble': 0, 'Model': 'UnobservedComponents', 'ModelParameters': '{"level": false, "trend": false, "cycle": true, "damped_cycle": false, "irregular": false, "stochastic_trend": false, "stochastic_level": true, "stochastic_cycle": false, "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}'}, '3': {'Ensemble': 0, 'Model': 'AverageValueNaive', 'ModelParameters': '{"method": "Mean"}', 'TransformationParameters': '{"fillna": "fake_date", "transformations": {"0": "DifferencedTransformer", "1": "SinTrend"}, "transformation_params": {"0": {}, "1": {}}}'}, '30': {'Ensemble': 0, 'Model': 'UnobservedComponents', 'ModelParameters': '{"level": true, "trend": false, "cycle": false, "damped_cycle": false, "irregular": false, "stochastic_trend": false, "stochastic_level": true, "stochastic_cycle": true, "regression_type": null}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 5, "fillna": null}}}'}, '31': {'Ensemble': 0, 'Model': 'VAR', 'ModelParameters': '{"regression_type": null, "maxlags": 5, "ic": "fpe"}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '32': {'Ensemble': 0, 'Model': 'VAR', 'ModelParameters': '{"regression_type": null, "maxlags": 15, "ic": "aic"}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "RollingMeanTransformer", "1": "QuantileTransformer", "2": "RollingMeanTransformer"}, "transformation_params": {"0": {"fixed": true, "window": 10}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"fixed": false, "window": 10}}}'}, '33': {'Ensemble': 0, 'Model': 'VECM', 'ModelParameters': '{"deterministic": "cili", "k_ar_diff": 2, "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "Detrend", "2": "Detrend", "3": "PowerTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"model": "GLS"}, "2": {"model": "Linear"}, "3": {}}}'}, '34': {'Ensemble': 0, 'Model': 'VECM', 'ModelParameters': '{"deterministic": "li", "k_ar_diff": 3, "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"model": "Linear"}}}'}, '35': {'Ensemble': 0, 'Model': 'WindowRegression', 'ModelParameters': '{"window_size": 10, "regression_model": {"model": "MLP", "model_params": {"hidden_layer_sizes": [72, 36, 72], "max_iter": 250, "activation": "relu", "solver": "lbfgs", "early_stopping": false, "learning_rate_init": 0.001}}, "input_dim": "univariate", "output_dim": "forecast_length", "normalize_window": false, "shuffle": true, "max_windows": 5000}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "QuantileTransformer", "1": "MinMaxScaler", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 100}, "1": {}, "2": {}}}'}, '36': {'Ensemble': 0, 'Model': 'ConstantNaive', 'ModelParameters': '{}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "PowerTransformer", "1": "QuantileTransformer", "2": "SeasonalDifference"}, "transformation_params": {"0": {}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"lag_1": 7, "method": "LastValue"}}}'}, '37': {'Ensemble': 0, 'Model': 'FBProphet', 'ModelParameters': '{"holiday": true, "regression_type": null, "growth": "linear", "n_changepoints": 30, "changepoint_prior_scale": 0.05, "seasonality_mode": "additive", "changepoint_range": 0.98, "seasonality_prior_scale": 10.0, "holidays_prior_scale": 10.0}', 'TransformationParameters': '{"fillna": "quadratic", "transformations": {"0": "QuantileTransformer", "1": "Slice", "2": "SeasonalDifference", "3": "Round"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 1000}, "1": {"method": 0.5}, "2": {"lag_1": 7, "method": "LastValue"}, "3": {"decimals": -1, "on_transform": false, "on_inverse": true}}}'}, '38': {'Ensemble': 0, 'Model': 'GluonTS', 'ModelParameters': '{"gluon_model": "Transformer", "epochs": 80, "learning_rate": 0.01, "context_length": "1ForecastLength", "regression_type": null}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "MaxAbsScaler"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {}}}'}, '39': {'Ensemble': 0, 'Model': 'MultivariateRegression', 'ModelParameters': '{"regression_model": {"model": "RandomForest", "model_params": {"n_estimators": 200, "min_samples_leaf": 1, "bootstrap": true}}, "mean_rolling_periods": 12, "macd_periods": 94, "std_rolling_periods": null, "max_rolling_periods": null, "min_rolling_periods": 7, "quantile90_rolling_periods": 10, "quantile10_rolling_periods": 30, "ewm_alpha": 0.1, "ewm_var_alpha": 0.2, "additional_lag_periods": null, "abs_energy": false, "rolling_autocorr_periods": null, "datepart_method": null, "polynomial_degree": null, "regression_type": null, "window": null, "holiday": true, "probabilistic": false}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "MaxAbsScaler", "2": "Detrend"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {}, "2": {"model": "Linear", "phi": 1, "window": null}}}'}, '4': {'Ensemble': 0, 'Model': 'AverageValueNaive', 'ModelParameters': '{"method": "Mean"}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}'}, '40': {'Ensemble': 0, 'Model': 'MultivariateRegression', 'ModelParameters': '{"regression_model": {"model": "LightGBM", "model_params": {"objective": "regression", "learning_rate": 0.1, "num_leaves": 70, "max_depth": -1, "boosting_type": "gbdt", "n_estimators": 100}}, "mean_rolling_periods": 12, "macd_periods": null, "std_rolling_periods": 30, "max_rolling_periods": null, "min_rolling_periods": 28, "quantile90_rolling_periods": 10, "quantile10_rolling_periods": null, "ewm_alpha": null, "ewm_var_alpha": null, "additional_lag_periods": null, "abs_energy": false, "rolling_autocorr_periods": null, "datepart_method": null, "polynomial_degree": null, "regression_type": "User", "window": 3, "holiday": false, "probabilistic": false}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "MaxAbsScaler", "2": "Detrend"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {}, "2": {"model": "Linear", "phi": 1, "window": null}}}'}, '41': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "ExtraTrees", "model_params": {"n_estimators": 500, "min_samples_leaf": 1, "max_depth": 10}}, "datepart_method": "expanded", "polynomial_degree": null, "regression_type": "User"}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "MaxAbsScaler", "1": "MinMaxScaler"}, "transformation_params": {"0": {}, "1": {}}}'}, '42': {'Ensemble': 0, 'Model': 'SeasonalNaive', 'ModelParameters': '{"method": "lastvalue", "lag_1": 364, "lag_2": 28}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}'}, '43': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "ExtraTrees", "model_params": {"n_estimators": 100, "min_samples_leaf": 1, "max_depth": null}}, "datepart_method": "recurring", "polynomial_degree": null, "regression_type": null}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}'}, '44': {'Ensemble': 0, 'Model': 'UnobservedComponents', 'ModelParameters': '{"level": false, "maxiter": 100, "cov_type": "opg", "method": "lbfgs", "autoregressive": null, "regression_type": "Holiday"}', 'TransformationParameters': '{"fillna": "median", "transformations": {"0": "Slice", "1": "SeasonalDifference"}, "transformation_params": {"0": {"method": 0.2}, "1": {"lag_1": 7, "method": "LastValue"}}}'}, '45': {'Ensemble': 0, 'Model': 'UnobservedComponents', 'ModelParameters': '{"level": "random walk with drift", "maxiter": 50, "cov_type": "opg", "method": "lbfgs", "autoregressive": null, "regression_type": "Holiday"}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "SeasonalDifference"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {"lag_1": 7, "method": "LastValue"}}}'}, '46': {'Ensemble': 0, 'Model': 'ETS', 'ModelParameters': '{"damped_trend": false, "trend": null, "seasonal": "additive", "seasonal_periods": 28}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "MaxAbsScaler", "1": "Slice"}, "transformation_params": {"0": {}, "1": {"method": 100}}}'}, '47': {'Ensemble': 0, 'Model': 'VECM', 'ModelParameters': '{"deterministic": "n", "k_ar_diff": 2, "regression_type": null}', 'TransformationParameters': '{"fillna": "rolling_mean", "transformations": {"0": "PCA", "1": "Detrend", "2": "HPFilter"}, "transformation_params": {"0": {}, "1": {"model": "GLS", "phi": 1, "window": null}, "2": {"part": "trend", "lamb": 129600}}}'}, '48': {'Ensemble': 0, 'Model': 'ARDL', 'ModelParameters': '{"lags": 4, "trend": "n", "order": 1, "regression_type": "holiday"}', 'TransformationParameters': '{"fillna": "fake_date", "transformations": {"0": "IntermittentOccurrence"}, "transformation_params": {"0": {"center": "mean"}}}'}, '49': {'Ensemble': 0, 'Model': 'MultivariateMotif', 'ModelParameters': '{"window": 10, "point_method": "median", "distance_metric": "mahalanobis", "k": 20, "max_windows": null}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "Detrend"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {"output_distribution": "normal", "n_quantiles": 1000}, "2": {"model": "Linear", "phi": 1, "window": null}}}'}, '5': {'Ensemble': 0, 'Model': 'AverageValueNaive', 'ModelParameters': '{"method": "Mean"}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "SeasonalDifference", "1": "Round", "2": "Detrend"}, "transformation_params": {"0": {"lag_1": 7, "method": "Mean"}, "1": {"model": "middle", "decimals": 2, "on_transform": true, "on_inverse": false}, "2": {"model": "GLS"}}}'}, '50': {'Ensemble': 0, 'Model': 'MultivariateMotif', 'ModelParameters': '{"window": 10, "point_method": "median", "distance_metric": "sqeuclidean", "k": 10, "max_windows": 10000}', 'TransformationParameters': '{"fillna": "rolling_mean", "transformations": {"0": "Detrend", "1": "bkfilter", "2": "Detrend"}, "transformation_params": {"0": {"model": "GLS", "phi": 1, "window": null}, "1": {}, "2": {"model": "Linear", "phi": 1, "window": null}}}'}, '51': {'Ensemble': 0, 'Model': 'UnivariateMotif', 'ModelParameters': '{"window": 60, "point_method": "median", "distance_metric": "canberra", "k": 10, "max_windows": 10000}', 'TransformationParameters': '{"fillna": "KNNImputer", "transformations": {"0": "QuantileTransformer", "1": "SeasonalDifference", "2": "MaxAbsScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 100}, "1": {"lag_1": 7, "method": "Mean"}, "2": {}}}'}, '52': {'Ensemble': 0, 'Model': 'UnivariateMotif', 'ModelParameters': '{"window": 14, "point_method": "median", "distance_metric": "minkowski", "k": 5, "max_windows": 10000}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "SeasonalDifference"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {"lag_1": 7, "method": "LastValue"}}}'}, '53': {'Ensemble': 0, 'Model': 'SectionalMotif', 'ModelParameters': '{"window": 10, "point_method": "weighted_mean", "distance_metric": "sokalmichener", "include_differenced": true, "k": 20, "stride_size": 1, "regression_type": null}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": null}, "transformation_params": {"0": {}}}'}, '54': {'Ensemble': 0, 'Model': 'SectionalMotif', 'ModelParameters': '{"window": 5, "point_method": "midhinge", "distance_metric": "canberra", "include_differenced": false, "k": 10, "stride_size": 1, "regression_type": null}', 'TransformationParameters': '{"fillna": "rolling_mean_24", "transformations": {"0": "QuantileTransformer", "1": "QuantileTransformer", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 1000}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}'}, '55': {'Ensemble': 0, 'Model': 'MultivariateRegression', 'ModelParameters': '{"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": null, "min_samples_split": 2}}, "mean_rolling_periods": 30, "macd_periods": 7, "std_rolling_periods": 90, "max_rolling_periods": null, "min_rolling_periods": null, "quantile90_rolling_periods": 10, "quantile10_rolling_periods": 90, "ewm_alpha": null, "ewm_var_alpha": null, "additional_lag_periods": null, "abs_energy": false, "rolling_autocorr_periods": null, "datepart_method": "recurring", "polynomial_degree": null, "regression_type": null, "window": 10, "holiday": true, "probabilistic": false}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "SeasonalDifference", "2": "Detrend"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {"model": "Linear", "phi": 1, "window": null}}}'}, '56': {'Ensemble': 0, 'Model': 'FBProphet', 'ModelParameters': '{"holiday": true, "regression_type": null, "growth": "linear", "n_changepoints": 25, "changepoint_prior_scale": 30, "seasonality_mode": "multiplicative", "changepoint_range": 0.9, "seasonality_prior_scale": 10.0, "holidays_prior_scale": 10.0}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "Slice"}, "transformation_params": {"0": {"method": 0.5}}}'}, '57': {'Ensemble': 0, 'Model': 'SeasonalNaive', 'ModelParameters': '{"method": "lastvalue", "lag_1": 364, "lag_2": 30}', 'TransformationParameters': '{"fillna": "fake_date", "transformations": {"0": "SeasonalDifference", "1": "MaxAbsScaler", "2": "Round"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}, "1": {}, "2": {"decimals": 0, "on_transform": false, "on_inverse": true}}}'}, '58': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "RandomForest", "model_params": {"n_estimators": 100, "min_samples_leaf": 2, "bootstrap": true}}, "datepart_method": "expanded", "polynomial_degree": null, "regression_type": null}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "QuantileTransformer", "1": "Detrend", "2": "RobustScaler"}, "transformation_params": {"0": {"output_distribution": "normal", "n_quantiles": 20}, "1": {"model": "Linear", "phi": 1, "window": null}, "2": {}}}'}, '59': {'Ensemble': 0, 'Model': 'NVAR', 'ModelParameters': '{"k": 1, "ridge_param": 0.002, "warmup_pts": 1, "seed_pts": 1, "seed_weighted": null, "batch_size": 5, "batch_method": "input_order"}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "MinMaxScaler"}, "transformation_params": {"0": {}}}'}, '6': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": 3, "min_samples_split": 2}}, "datepart_method": "recurring", "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer", "2": "DifferencedTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {}}}'}, '60': {'Ensemble': 0, 'Model': 'Theta', 'ModelParameters': '{"deseasonalize": true, "difference": false, "use_test": false, "method": "auto", "period": null, "theta": 2.5, "use_mle": true}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "Detrend"}, "transformation_params": {"0": {"model": "Linear", "phi": 1, "window": 90}}}'}, '61': {'Ensemble': 0, 'Model': 'RollingRegression', 'ModelParameters': '{"regression_model": {"model": "ExtraTrees", "model_params": {"n_estimators": 100, "min_samples_leaf": 1, "max_depth": 10}}, "holiday": false, "mean_rolling_periods": null, "macd_periods": null, "std_rolling_periods": null, "max_rolling_periods": null, "min_rolling_periods": 7, "ewm_var_alpha": null, "quantile90_rolling_periods": null, "quantile10_rolling_periods": null, "ewm_alpha": null, "additional_lag_periods": 95, "abs_energy": true, "rolling_autocorr_periods": null, "add_date_part": "expanded", "polynomial_degree": null, "x_transform": null, "regression_type": "User"}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "MaxAbsScaler"}, "transformation_params": {"0": {}}}'}, '62': {'Ensemble': 0, 'Model': 'UnivariateRegression', 'ModelParameters': '{"regression_model": {"model": "DecisionTree", "model_params": {"max_depth": null, "min_samples_split": 1.0}}, "holiday": false, "mean_rolling_periods": null, "macd_periods": null, "std_rolling_periods": null, "max_rolling_periods": 12, "min_rolling_periods": 58, "ewm_var_alpha": null, "ewm_alpha": 0.5, "additional_lag_periods": 363, "abs_energy": false, "rolling_autocorr_periods": null, "add_date_part": "simple_2_poly", "polynomial_degree": null, "x_transform": null, "regression_type": null, "window": null}', 'TransformationParameters': '{"fillna": "rolling_mean", "transformations": {"0": "MaxAbsScaler", "1": "SeasonalDifference", "2": "Round"}, "transformation_params": {"0": {}, "1": {"lag_1": 7, "method": "LastValue"}, "2": {"decimals": -2, "on_transform": false, "on_inverse": true}}}'}, '63': {'Ensemble': 0, 'Model': 'MotifSimulation', 'ModelParameters': '{"phrase_len": 10, "comparison": "magnitude_pct_change_sign", "shared": false, "distance_metric": "sokalmichener", "max_motifs": 0.2, "recency_weighting": 0.01, "cutoff_minimum": 20, "point_method": "median"}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "MaxAbsScaler", "1": "Detrend"}, "transformation_params": {"0": {}, "1": {"model": "Linear", "phi": 1, "window": null}}}'}, '64': {'Ensemble': 0, 'Model': 'DynamicFactor', 'ModelParameters': '{"k_factors": 0, "factor_order": 0, "regression_type": "User"}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "MaxAbsScaler", "1": "EWMAFilter", "2": "QuantileTransformer"}, "transformation_params": {"0": {}, "1": {"span": 3}, "2": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '65': {'Ensemble': 0, 'Model': 'RollingRegression', 'ModelParameters': '{"regression_model": {"model": "ExtraTrees", "model_params": {"n_estimators": 100, "min_samples_leaf": 1, "max_depth": 10}}, "holiday": false, "mean_rolling_periods": null, "macd_periods": null, "std_rolling_periods": null, "max_rolling_periods": 420, "min_rolling_periods": 7, "ewm_var_alpha": null, "quantile90_rolling_periods": null, "quantile10_rolling_periods": null, "ewm_alpha": null, "additional_lag_periods": 363, "abs_energy": false, "rolling_autocorr_periods": null, "add_date_part": "expanded", "polynomial_degree": null, "x_transform": null, "regression_type": null}', 'TransformationParameters': '{"fillna": "ffill_mean_biased", "transformations": {"0": "RobustScaler"}, "transformation_params": {"0": {}}}'}, '66': {'Ensemble': 0, 'Model': 'ARCH', 'ModelParameters': '{"mean": "Zero", "lags": 1, "vol": "GARCH", "p": 4, "o": 1, "q": 2, "power": 1.5, "dist": "studentst", "rescale": true, "simulations": 1000, "maxiter": 200, "regression_type": null}', 'TransformationParameters': '{"fillna": "ffill", "transformations": {"0": "SeasonalDifference"}, "transformation_params": {"0": {"lag_1": 7, "method": "LastValue"}}}'}, '67': {'Ensemble': 0, 'Model': 'Cassandra', 'ModelParameters': '{\n            "preprocessing_transformation": {"fillna": "ffill", "transformations": {"0": "ClipOutliers"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}}}, \n            "scaling": {"fillna": null, "transformations": {"0": "QuantileTransformer"}, "transformation_params": {"0": {"output_distribution": "uniform", "n_quantiles": 308}}},\n            "past_impacts_intervention": null,\n            "seasonalities": ["month", "dayofweek", "weekdayofmonth"],\n            "ar_lags": null, "ar_interaction_seasonality": null,\n            "anomaly_detector_params": {"method": "zscore", "transform_dict": {"transformations": {"0": "DatepartRegression"}, "transformation_params": {"0": {"datepart_method": "simple_3", "regression_model": {"model": "ElasticNet", "model_params": {}}}}}, "method_params": {"distribution": "gamma", "alpha": 0.05}, "fillna": "rolling_mean_24"}, "anomaly_intervention": null,\n            "holiday_detector_params": {"threshold": 0.9, "splash_threshold": null, "use_dayofmonth_holidays": true, "use_wkdom_holidays": true, "use_wkdeom_holidays": false, "use_lunar_holidays": false, "use_lunar_weekday": false, "use_islamic_holidays": false, "use_hebrew_holidays": false, "anomaly_detector_params": {"method": "IQR", "transform_dict": {"fillna": "rolling_mean_24", "transformations": {"0": "bkfilter", "1": "PCA"}, "transformation_params": {"0": {}, "1": {"whiten": true}}}, "method_params": {"iqr_threshold": 2.0, "iqr_quantiles": [0.25, 0.75]}, "fillna": "linear"}, "remove_excess_anomalies": false, "impact": null, "regression_params": null, "output": "multivariate"},\n            "holiday_countries_used": false,\n            "multivariate_feature": null, "multivariate_transformation": null, "regressor_transformation": null,\n            "regressors_used": false, "linear_model": {"model": "linalg_solve", "lambda": 0.1, "recency_weighting": 0.1}, "randomwalk_n": 10, "trend_window": 90,\n            "trend_standin": null,\n            "trend_anomaly_detector_params": {"method": "zscore", "transform_dict": {"transformations": {"0": "DifferencedTransformer"}, "transformation_params": {"0": {}}}, "method_params": {"distribution": "norm", "alpha": 0.05}, "fillna": "rolling_mean_24"},\n            "trend_transformation": {"fillna": "ffill_mean_biased", "transformations": {"0": "bkfilter"}, "transformation_params": {"0": {}}},\n            "trend_model": {"Model": "MetricMotif", "ModelParameters": {"window": 7, "point_method": "mean", "distance_metric": "mae", "k": 10, "comparison_transformation": {"fillna": "quadratic", "transformations": {"0": null}, "transformation_params": {"0": {}}}, "combination_transformation": {"fillna": "ffill", "transformations": {"0": "AlignLastValue"}, "transformation_params": {"0": {"rows": 1, "lag": 1, "method": "additive", "strength": 1.0, "first_value_only": false}}}}},\n            "trend_phi": null\n        }', 'TransformationParameters': '{"fillna": null, "transformations": {}, "transformation_params": {}}'}, '7': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "SVM", "model_params": {}}, "datepart_method": "recurring", "regression_type": null}', 'TransformationParameters': '{"fillna": "zero", "transformations": {"0": "ClipOutliers", "1": "RollingMeanTransformer", "2": "Detrend", "3": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3.5, "fillna": null}, "1": {"fixed": true, "window": 10}, "2": {"model": "Linear"}, "3": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '8': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "MLP", "model_params": {"hidden_layer_sizes": [25, 15, 25], "max_iter": 1000, "activation": "tanh", "solver": "lbfgs", "early_stopping": false, "learning_rate_init": 0.001}}, "datepart_method": "recurring", "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "ClipOutliers", "1": "QuantileTransformer"}, "transformation_params": {"0": {"method": "clip", "std_threshold": 3, "fillna": null}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}}}'}, '9': {'Ensemble': 0, 'Model': 'DatepartRegression', 'ModelParameters': '{"regression_model": {"model": "KerasRNN", "model_params": {"kernel_initializer": "glorot_uniform", "epochs": 50, "batch_size": 32, "optimizer": "adam", "loss": "Huber", "hidden_layer_sizes": [32, 32, 32], "rnn_type": "GRU"}}, "datepart_method": "recurring", "regression_type": null}', 'TransformationParameters': '{"fillna": "mean", "transformations": {"0": "Round", "1": "QuantileTransformer", "2": "QuantileTransformer", "3": "QuantileTransformer", "4": "PowerTransformer"}, "transformation_params": {"0": {"model": "middle", "decimals": 1, "on_transform": true, "on_inverse": false}, "1": {"output_distribution": "uniform", "n_quantiles": 1000}, "2": {"output_distribution": "uniform", "n_quantiles": 20}, "3": {"output_distribution": "normal", "n_quantiles": 1000}, "4": {}}}'}}
      -
      -
      {

      ‘Model’: ‘UnivariateRegression’, -‘ModelParameters’: ‘{“regression_model”: {“model”: “KNN”, “model_params”: {“n_neighbors”: 3, “weights”: “uniform”}}, “holiday”: false, “mean_rolling_periods”: 5, “macd_periods”: 28, “std_rolling_periods”: null, “max_rolling_periods”: 24, “min_rolling_periods”: null, “ewm_var_alpha”: null, “ewm_alpha”: 0.5, “additional_lag_periods”: 11, “abs_energy”: false, “rolling_autocorr_periods”: null, “add_date_part”: null, “polynomial_degree”: null, “x_transform”: null, “regression_type”: null, “window”: null}’, -‘TransformationParameters’: ‘{“fillna”: “rolling_mean”, “transformations”: {“0”: “QuantileTransformer”, “1”: “QuantileTransformer”}, “transformation_params”: {“0”: {“output_distribution”: “normal”, “n_quantiles”: 1000}, “1”: {“output_distribution”: “uniform”, “n_quantiles”: 20}}}’, -‘Ensemble’: 0,

      -
      -
      -

      },

      -
      -
      Type:
      -

      “66”

      -
      -
      -

      -

      Module contents

      diff --git a/docs/build/html/source/autots.tools.html b/docs/build/html/source/autots.tools.html index 44691d51..c44c8cc7 100644 --- a/docs/build/html/source/autots.tools.html +++ b/docs/build/html/source/autots.tools.html @@ -6,7 +6,7 @@ - autots.tools package — AutoTS 0.5.8 documentation + autots.tools package — AutoTS 0.6.0 documentation @@ -862,6 +862,18 @@

      Usage example +
      +class autots.tools.impute.SeasonalityMotifImputer(k: int = 3, datepart_method: str = 'simple_2', distance_metric: str = 'canberra', linear_mixed: bool = False)
      +

      Bases: object

      +
      +
      +impute(df)
      +

      Infer missing values on input df.

      +
      + +

      +
      autots.tools.impute.biased_ffill(df, mean_weight: float = 1)
      @@ -1173,7 +1185,7 @@

      Usage example
      -autots.tools.regressor.create_regressor(df, forecast_length, frequency: str = 'infer', holiday_countries: list = ['US'], datepart_method: str = 'simple_binarized', drop_most_recent: int = 0, scale: bool = True, summarize: str = 'auto', backfill: str = 'bfill', n_jobs: str = 'auto', fill_na: str = 'ffill', aggfunc: str = 'first', encode_holiday_type=False, holiday_detector_params={'anomaly_detector_params': {'forecast_params': None, 'method': 'mad', 'method_params': {'alpha': 0.05, 'distribution': 'gamma'}, 'transform_dict': {'fillna': None, 'transformation_params': {'0': {}}, 'transformations': {'0': 'DifferencedTransformer'}}}, 'output': 'univariate', 'splash_threshold': None, 'threshold': 0.8, 'use_dayofmonth_holidays': True, 'use_hebrew_holidays': False, 'use_islamic_holidays': False, 'use_lunar_holidays': False, 'use_lunar_weekday': False, 'use_wkdeom_holidays': False, 'use_wkdom_holidays': True}, holiday_regr_style='flag')
      +autots.tools.regressor.create_regressor(df, forecast_length, frequency: str = 'infer', holiday_countries: list = ['US'], datepart_method: str = 'simple_binarized', drop_most_recent: int = 0, scale: bool = True, summarize: str = 'auto', backfill: str = 'bfill', n_jobs: str = 'auto', fill_na: str = 'ffill', aggfunc: str = 'first', encode_holiday_type=False, holiday_detector_params={'anomaly_detector_params': {'forecast_params': None, 'method': 'mad', 'method_params': {'alpha': 0.05, 'distribution': 'gamma'}, 'transform_dict': {'fillna': None, 'transformation_params': {'0': {}}, 'transformations': {'0': 'DifferencedTransformer'}}}, 'output': 'univariate', 'splash_threshold': None, 'threshold': 0.8, 'use_dayofmonth_holidays': True, 'use_hebrew_holidays': False, 'use_islamic_holidays': False, 'use_lunar_holidays': False, 'use_lunar_weekday': False, 'use_wkdeom_holidays': False, 'use_wkdom_holidays': True}, holiday_regr_style: str = 'flag', preprocessing_params: dict | None = None)

      Create a regressor from information available in the existing dataset. Components: are lagged data, datepart information, and holiday.

      This function has been confusing people. This is NOT necessary for machine learning models, in AutoTS they internally create more elaborate feature sets separately. @@ -1204,6 +1216,7 @@

      Usage exampleReturns: @@ -1266,6 +1279,11 @@

      Usage exampleautots.tools.seasonal.fourier_series(t, p=365.25, n=10)

      +
      +
      +autots.tools.seasonal.seasonal_independent_match(DTindex, DTindex_future, k, datepart_method, distance_metric)
      +
      +
      autots.tools.seasonal.seasonal_int(include_one: bool = False, small=False, very_small=False)
      @@ -2034,6 +2052,12 @@

      Usage example +
      +impute(df, regressor=None)
      +

      Fill Missing. Needs to have same general pattern of missingness (full rows of NaN only or scattered NaN) as was present during .fit()

      +

      +
      inverse_transform(df, regressor=None)
      @@ -2813,9 +2837,81 @@

      Usage example +
      +class autots.tools.transform.LevelShiftMagic(window_size: int = 90, alpha: float = 2.5, grouping_forward_limit: int = 3, max_level_shifts: int = 20, alignment: str = 'average', **kwargs)
      +

      Bases: EmptyTransformer

      +

      Detects and corrects for level shifts.

      +
      +
      Parameters:
      +
        +
      • method (str) – “clip” or “remove”

      • +
      • std_threshold (float) – number of std devs from mean to call an outlier

      • +
      • fillna (str) – fillna method to use per tools.impute.FillNA

      • +
      +
      +
      +
      +
      +fit(df)
      +

      Learn behavior of data to change.

      +
      +
      Parameters:
      +

      df (pandas.DataFrame) – input dataframe

      +
      +
      +
      + +
      +
      +fit_transform(df)
      +

      Fits and Returns Magical DataFrame.

      +
      +
      Parameters:
      +

      df (pandas.DataFrame) – input dataframe

      +
      +
      +
      + +
      +
      +static get_new_params(method: str = 'random')
      +

      Generate new random parameters

      +
      + +
      +
      +inverse_transform(df, trans_method: str = 'forecast')
      +

      Return data to original or forecast form.

      +
      +
      Parameters:
      +

      df (pandas.DataFrame) – input dataframe

      +
      +
      +
      + +
      +
      +transform(df)
      +

      Return changed data.

      +
      +
      Parameters:
      +

      df (pandas.DataFrame) – input dataframe

      +
      +
      +
      + +

      + +
      +
      +autots.tools.transform.LevelShiftTransformer
      +

      alias of LevelShiftMagic

      +
      +
      -class autots.tools.transform.LocalLinearTrend(rolling_window: float = 0.1, n_future: float = 0.2, method: str = 'mean', **kwargs)
      +class autots.tools.transform.LocalLinearTrend(rolling_window: float = 0.1, n_future: float = 0.2, method: str = 'mean', macro_micro: bool = False, suffix: str = '_lltmicro', **kwargs)

      Bases: EmptyTransformer

      Remove a rolling linear trend. Note this will probably perform poorly with long forecast horizons as forecast trend is simply the tail (n_future) of data’s trend.

      @@ -2824,6 +2920,7 @@

      Usage example
      • rolling_window (int) – width of window to take trend on

      • n_future (int) – amount of data for the trend to be used extending beyond the edges of history.

      • +
      • macro_micro (bool) – when True, splits the data into separate parts (trend and residual) and later combines together in inverse

      @@ -3115,7 +3212,7 @@

      Usage example
      -autots.tools.transform.RandomTransform(transformer_list: dict = {None: 0.0, 'MinMaxScaler': 0.05, 'PowerTransformer': 0.02, 'QuantileTransformer': 0.05, 'MaxAbsScaler': 0.05, 'StandardScaler': 0.04, 'RobustScaler': 0.05, 'PCA': 0.01, 'FastICA': 0.01, 'Detrend': 0.1, 'RollingMeanTransformer': 0.02, 'RollingMean100thN': 0.01, 'DifferencedTransformer': 0.07, 'SinTrend': 0.01, 'PctChangeTransformer': 0.01, 'CumSumTransformer': 0.02, 'PositiveShift': 0.02, 'Log': 0.01, 'IntermittentOccurrence': 0.01, 'SeasonalDifference': 0.1, 'cffilter': 0.01, 'bkfilter': 0.05, 'convolution_filter': 0.001, 'HPFilter': 0.01, 'DatepartRegression': 0.01, 'ClipOutliers': 0.05, 'Discretize': 0.01, 'CenterLastValue': 0.01, 'Round': 0.02, 'Slice': 0.02, 'ScipyFilter': 0.02, 'STLFilter': 0.01, 'EWMAFilter': 0.02, 'MeanDifference': 0.002, 'BTCD': 0.01, 'Cointegration': 0.01, 'AlignLastValue': 0.2, 'AnomalyRemoval': 0.03, 'HolidayTransformer': 0.01, 'LocalLinearTrend': 0.01, 'KalmanSmoothing': 0.04, 'RegressionFilter': 0.03}, transformer_max_depth: int = 4, na_prob_dict: dict = {'ffill': 0.4, 'fake_date': 0.1, 'rolling_mean': 0.1, 'rolling_mean_24': 0.1, 'IterativeImputer': 0.05, 'mean': 0.06, 'zero': 0.05, 'ffill_mean_biased': 0.1, 'median': 0.03, None: 0.001, 'interpolate': 0.4, 'KNNImputer': 0.05, 'IterativeImputerExtraTrees': 0.0001}, fast_params: bool | None = None, superfast_params: bool | None = None, traditional_order: bool = False, transformer_min_depth: int = 1, allow_none: bool = True, no_nan_fill: bool = False)
      +autots.tools.transform.RandomTransform(transformer_list: dict = {None: 0.0, 'MinMaxScaler': 0.05, 'PowerTransformer': 0.02, 'QuantileTransformer': 0.05, 'MaxAbsScaler': 0.05, 'StandardScaler': 0.04, 'RobustScaler': 0.05, 'PCA': 0.01, 'FastICA': 0.01, 'Detrend': 0.1, 'RollingMeanTransformer': 0.02, 'RollingMean100thN': 0.01, 'DifferencedTransformer': 0.07, 'SinTrend': 0.01, 'PctChangeTransformer': 0.01, 'CumSumTransformer': 0.02, 'PositiveShift': 0.02, 'Log': 0.01, 'IntermittentOccurrence': 0.01, 'SeasonalDifference': 0.1, 'cffilter': 0.01, 'bkfilter': 0.05, 'convolution_filter': 0.001, 'HPFilter': 0.01, 'DatepartRegression': 0.01, 'ClipOutliers': 0.05, 'Discretize': 0.01, 'CenterLastValue': 0.01, 'Round': 0.02, 'Slice': 0.02, 'ScipyFilter': 0.02, 'STLFilter': 0.01, 'EWMAFilter': 0.02, 'MeanDifference': 0.002, 'BTCD': 0.01, 'Cointegration': 0.01, 'AlignLastValue': 0.2, 'AnomalyRemoval': 0.03, 'HolidayTransformer': 0.01, 'LocalLinearTrend': 0.01, 'KalmanSmoothing': 0.04, 'RegressionFilter': 0.07, 'LevelShiftTransformer': 0.03}, transformer_max_depth: int = 4, na_prob_dict: dict = {'ffill': 0.4, 'fake_date': 0.1, 'rolling_mean': 0.1, 'rolling_mean_24': 0.1, 'IterativeImputer': 0.05, 'mean': 0.06, 'zero': 0.05, 'ffill_mean_biased': 0.1, 'median': 0.03, None: 0.001, 'interpolate': 0.4, 'KNNImputer': 0.05, 'IterativeImputerExtraTrees': 0.0001, 'SeasonalityMotifImputer': 0.1, 'SeasonalityMotifImputerLinMix': 0.02, 'DatepartRegressionImputer': 0.05}, fast_params: bool | None = None, superfast_params: bool | None = None, traditional_order: bool = False, transformer_min_depth: int = 1, allow_none: bool = True, no_nan_fill: bool = False)

      Return a dict of randomly choosen transformation selections.

      BTCD is used as a signal that slow parameters are allowed.

      diff --git a/docs/build/html/source/intro.html b/docs/build/html/source/intro.html index 086530bc..fcc66df1 100644 --- a/docs/build/html/source/intro.html +++ b/docs/build/html/source/intro.html @@ -6,7 +6,7 @@ - Intro — AutoTS 0.5.8 documentation + Intro — AutoTS 0.6.0 documentation @@ -93,7 +93,7 @@

      Basic Useforecast_length=21, frequency='infer', prediction_interval=0.9, - ensemble=None, + ensemble='auto', model_list="fast", # "superfast", "default", "fast_parallel" transformer_list="fast", # "superfast", drop_most_recent=1, diff --git a/docs/build/html/source/modules.html b/docs/build/html/source/modules.html index 69a215c4..d9de4222 100644 --- a/docs/build/html/source/modules.html +++ b/docs/build/html/source/modules.html @@ -6,7 +6,7 @@ - autots — AutoTS 0.5.8 documentation + autots — AutoTS 0.6.0 documentation @@ -130,16 +130,21 @@

      autotsAutoTS.back_forecast()
    • AutoTS.best_model_per_series_mape()
    • AutoTS.best_model_per_series_score()
    • +
    • AutoTS.export_best_model()
    • AutoTS.export_template()
    • AutoTS.failure_rate()
    • AutoTS.fit()
    • +
    • AutoTS.fit_data()
    • AutoTS.get_new_params()
    • AutoTS.horizontal_per_generation()
    • AutoTS.horizontal_to_df()
    • +
    • AutoTS.import_best_model()
    • AutoTS.import_results()
    • AutoTS.import_template()
    • AutoTS.list_failed_model_types()
    • +
    • AutoTS.load_template()
    • AutoTS.mosaic_to_df()
    • +
    • AutoTS.parse_best_model()
    • AutoTS.plot_back_forecast()
    • AutoTS.plot_backforecast()
    • AutoTS.plot_generation_loss()
    • @@ -149,9 +154,11 @@

      autotsAutoTS.plot_horizontal_transformers()
    • AutoTS.plot_per_series_error()
    • AutoTS.plot_per_series_mape()
    • +
    • AutoTS.plot_per_series_smape()
    • AutoTS.plot_validations()
    • AutoTS.predict()
    • AutoTS.results()
    • +
    • AutoTS.save_template()
  • AutoTS.initial_results.model_results
  • @@ -172,6 +179,7 @@

    autotsCassandra.cross_validate()
  • Cassandra.feature_importance()
  • Cassandra.fit()
  • +
  • Cassandra.fit_data()
  • Cassandra.get_new_params()
  • Cassandra.get_params()
  • Cassandra.next_fit()
  • diff --git a/docs/build/html/source/tutorial.html b/docs/build/html/source/tutorial.html index c556aba9..d19da526 100644 --- a/docs/build/html/source/tutorial.html +++ b/docs/build/html/source/tutorial.html @@ -6,7 +6,7 @@ - Tutorial — AutoTS 0.5.8 documentation + Tutorial — AutoTS 0.6.0 documentation @@ -311,7 +311,7 @@

    Metrics ) plt.show() -model.plot_per_series_smape(kind="pie") +model.plot_per_series_mape(kind="pie") plt.show() model.plot_per_series_error() @@ -534,10 +534,11 @@

    Caveats and Advice

    Mysterious crashes

    Usually mysterious crashes or hangs (those without clear error messages) occur when the CPU or Memory is overloaded. -UnivariateRegression is usually the most prone to these issues, removing it from the model_list may help.

    +UnivariateRegression is usually the most prone to these issues, removing it from the model_list may help (by default it is not included in most lists for this reason).

    Try setting n_jobs=1 or an otherwise low number, which should reduce the load. Also test the ‘superfast’ naive models, which are generally low resource consumption. GPU-accelerated models (Tensorflow in Regressions and GluonTS) are also more prone to crashes, and may be a source of problems when used. If problems persist, post to the GitHub Discussion or Issues.

    +

    Rebooting between heavy uses of multiprocessing can also help reduce the risk of crashing in future model runs.

    Series IDs really need to be unique (or column names need to be all unique in wide data)

    @@ -758,6 +759,7 @@

    Event Risk Forecasting and Anomaly Detectionmod = AnomalyDetector(output='multivariate', **params) mod.detect(df) mod.plot() +mod.scores # meaning of scores varies by method # holiday detection, random parameters holiday_params = HolidayDetector.get_new_params() diff --git a/docs/conf.py b/docs/conf.py index 32bc610b..f297e508 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -25,7 +25,7 @@ # import AutoTS # from AutoTS import __version__ # release = __version__ -release = "0.5.8" +release = "0.6.0" # -- General configuration --------------------------------------------------- diff --git a/extended_tutorial.md b/extended_tutorial.md index 5d72c2f1..1fca811b 100644 --- a/extended_tutorial.md +++ b/extended_tutorial.md @@ -752,6 +752,7 @@ params = AnomalyDetector.get_new_params() mod = AnomalyDetector(output='multivariate', **params) mod.detect(df) mod.plot() +mod.scores # meaning of scores varies by method # holiday detection, random parameters holiday_params = HolidayDetector.get_new_params() diff --git a/production_example.py b/production_example.py index 89cb394c..ca994fd6 100644 --- a/production_example.py +++ b/production_example.py @@ -36,7 +36,7 @@ graph = True # whether to plot graphs # https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects frequency = ( - "D" # "infer" for automatic alignment, but specific offsets are most reliable + "D" # "infer" for automatic alignment, but specific offsets are most reliable, 'D' is daily ) forecast_length = 60 # number of periods to forecast ahead drop_most_recent = 1 # whether to discard the n most recent records (as incomplete) @@ -124,6 +124,7 @@ tickers = ["MSFT", "PG"] trend_list = ["forecasting", "msft", "p&g"] weather_event_types = ["%28Z%29+Winter+Weather", "%28Z%29+Winter+Storm"] +wikipedia_pages = ['all', 'Microsoft', "Procter_%26_Gamble", "YouTube", "United_States"] df = load_live_daily( long=False, fred_key=fred_key, @@ -133,7 +134,7 @@ earthquake_min_magnitude=5, weather_years=3, london_air_days=700, - wikipedia_pages=['all', 'Microsoft', "Procter_%26_Gamble", "YouTube", "United_States"], + wikipedia_pages=wikipedia_pages, gsa_key=gsa_key, gov_domain_list=None, # ['usajobs.gov', 'usps.com', 'weather.gov'], gov_domain_limit=700, @@ -196,6 +197,7 @@ # example future_regressor with some things we can glean from data and datetime index # note this only accepts `wide` style input dataframes # and this is optional, not required for the modeling +# also create macro_micro before inclusion regr_train, regr_fcst = create_regressor( df, forecast_length=forecast_length, @@ -243,7 +245,7 @@ max_generations=gens, metric_weighting=metric_weighting, initial_template=initial_template, - aggfunc="sum", + aggfunc="first", models_to_validate=models_to_validate, model_interrupt=True, num_validations=num_validations, @@ -273,6 +275,19 @@ future_regressor=regr_train, ) +# save a template of best models +if initial_training or evolve: + model.export_template( + template_filename, + models="best", + n=n_export, + max_per_model_class=6, + include_results=True, + ) + if archive_templates: + arc_file = f"{template_filename.split('.csv')[0]}_{start_time.strftime('%Y%m%d%H%M')}.csv" + model.export_template(arc_file, models="best", n=1) + prediction = model.predict( future_regressor=regr_fcst, verbose=2, fail_on_forecast_nan=True ) @@ -296,19 +311,6 @@ model_results = model.results() validation_results = model.results("validation") -# save a template of best models -if initial_training or evolve: - model.export_template( - template_filename, - models="best", - n=n_export, - max_per_model_class=6, - include_results=True, - ) - if archive_templates: - arc_file = f"{template_filename.split('.csv')[0]}_{start_time.strftime('%Y%m%d%H%M')}.csv" - model.export_template(arc_file, models="best", n=1) - print(f"Model failure rate is {model.failure_rate() * 100:.1f}%") print(f'The following model types failed completely {model.list_failed_model_types()}') print("Slowest models:") @@ -329,11 +331,13 @@ prediction.plot_grid(model.df_wide_numeric, start_date=start_date) plt.show() - worst = model.best_model_per_series_score().head(6).index.tolist() + scores = model.best_model_per_series_mape().index.tolist() + scores = [x for x in scores if x in df.columns] + worst = scores[0:6] prediction.plot_grid(model.df_wide_numeric, start_date=start_date, title="Worst Performing Forecasts", cols=worst) plt.show() - best = model.best_model_per_series_score().tail(6).index.tolist() + best = scores[-6:] prediction.plot_grid(model.df_wide_numeric, start_date=start_date, title="Best Performing Forecasts", cols=best) plt.show() diff --git a/pyproject.toml b/pyproject.toml index 734b07b0..7c31d96c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "autots" -version = "0.5.8" +version = "0.6.0" authors = [ { name="Colin Catlin", email="colin.catlin@gmail.com" }, ] diff --git a/setup.py b/setup.py index 7e3caf39..5d66747f 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ setuptools.setup( name="AutoTS", - version="0.5.8", + version="0.6.0", author="Colin Catlin", author_email="colin.catlin@syllepsis.live", description="Automated Time Series Forecasting", diff --git a/test.py b/test.py index 13ad8492..eee0fdcd 100644 --- a/test.py +++ b/test.py @@ -62,7 +62,7 @@ validation_method = "similarity" # "similarity" frequency = "infer" drop_most_recent = 0 -generations = 20 +generations = 100 generation_timeout = 300 num_validations = 2 # "auto" initial_template = "Random" # "General+Random" @@ -77,8 +77,8 @@ df = df.iloc[:, 0] transformer_list = "fast" # "fast", "all", "superfast" -# transformer_list = ["SeasonalDifference", "Slice", "EWMAFilter", 'MinMaxScaler', "AlignLastValue", "RegressionFilter", "ClipOutliers", "QuantileTransformer", "DatepartRegression"] -transformer_max_depth = 1 +# transformer_list = ["SeasonalDifference", "Slice", "EWMAFilter", 'MinMaxScaler', "AlignLastValue", "RegressionFilter", "ClipOutliers", "QuantileTransformer", "LevelShiftTransformer"] +transformer_max_depth = 4 models_mode = "default" # "default", "regressor", "neuralnets", "gradient_boosting" model_list = "superfast" # model_list = "fast_parallel" # fast_parallel, all, fast @@ -86,10 +86,16 @@ # model_list = ['LastValueNaive', 'PytorchForecasting'] preclean = None { - "fillna": None, # mean or median one of few consistent things - "transformations": {"0": "EWMAFilter"}, + "fillna": None, + "transformations": {"0": "LocalLinearTrend"}, "transformation_params": { - "0": {"span": 14}, + "0": { + 'rolling_window': 30, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True + }, }, } ensemble = [ @@ -111,7 +117,7 @@ 'spl_weighting': 3, 'containment_weighting': 0, 'contour_weighting': 0, - 'runtime_weighting': 0.05, + 'runtime_weighting': 0.01, 'maxe_weighting': 0, 'oda_weighting': 0, 'mqae_weighting': 0, @@ -186,9 +192,21 @@ fill_na="pchip", holiday_countries=["US"], datepart_method="recurring", + preprocessing_params={ + "fillna": None, + "transformations": {"0": "LocalLinearTrend"}, + "transformation_params": { + "0": { + 'rolling_window': 30, + 'n_tails': 0.1, + 'n_future': 0.2, + 'method': 'mean', + 'macro_micro': True + }, + }, + }, ) - # model = model.import_results('test.pickle') if use_template: model = model.import_template( @@ -250,22 +268,26 @@ if graph: start_date = "auto" + # issues with long and preclean vary 'raw' df choice + use_df = df if not long else model.df_wide_numeric prediction.plot( - model.df_wide_numeric, + use_df, series=cols[0], remove_zeroes=False, start_date=start_date, ) # plt.savefig("single_forecast2.png", dpi=300, bbox_inches="tight") plt.show() - prediction.plot_grid(model.df_wide_numeric, start_date=start_date) + prediction.plot_grid(use_df, start_date=start_date) # plt.savefig("forecast_grid2.png", dpi=300, bbox_inches="tight") plt.show() - worst = model.best_model_per_series_score().head(6).index.tolist() - prediction.plot_grid(model.df_wide_numeric, start_date=start_date, title="Forecasts of Highest (Worst) Historical MAPE Series", cols=worst) + scores = model.best_model_per_series_mape().index.tolist() + scores = [x for x in scores if x in df.columns] + worst = scores[0:6] + prediction.plot_grid(use_df, start_date=start_date, title="Forecasts of Highest (Worst) Historical MAPE Series", cols=worst) plt.show() - best = model.best_model_per_series_score().tail(6).index.tolist() - prediction.plot_grid(model.df_wide_numeric, start_date=start_date, title="Forecasts of Lowest (Best) Historical MAPE Series", cols=best) + best = scores[-6:] + prediction.plot_grid(use_df, start_date=start_date, title="Forecasts of Lowest (Best) Historical MAPE Series", cols=best) plt.show() model.plot_generation_loss() plt.show() @@ -304,29 +326,29 @@ if back_forecast: model.plot_backforecast(n_splits="auto", start_date="2019-01-01") - ax = model.plot_validations(subset='Worst', compare_horizontal=True, include_bounds=False) + ax = model.plot_validations(use_df, subset='Worst', compare_horizontal=True, include_bounds=False) plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) plt.show() - ax = model.plot_validations() + ax = model.plot_validations(use_df) plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) # plt.savefig("validation_plot.png", dpi=300, bbox_inches="tight") plt.show() - ax = model.plot_validations(subset='Best') + ax = model.plot_validations(use_df, subset='Best') plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) # plt.savefig("validation_plot2.png", dpi=300, bbox_inches="tight") plt.show() - ax = model.plot_validations(subset='Worst') + ax = model.plot_validations(use_df, subset='Worst') plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) plt.show() - ax = model.plot_validations(subset='Best Score') + ax = model.plot_validations(use_df, subset='Best Score') plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) plt.show() - ax = model.plot_validations(subset='Worst Score') + ax = model.plot_validations(use_df, subset='Worst Score') plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0) plt.show() @@ -344,7 +366,7 @@ interest_models.extend(list(y.values())) if graph: prediction.plot( - model.df_wide_numeric, + use_df, series=x, remove_zeroes=False, start_date=start_date, @@ -362,7 +384,7 @@ for x in interest_series: if graph: prediction.plot( - model.df_wide_numeric, + use_df, series=x, remove_zeroes=False, start_date=start_date, @@ -425,6 +447,8 @@ python -m unittest tests.test_autots.ModelTest.test_models python -m unittest tests.test_impute.TestImpute.test_impute +pytest tests/ --durations=0 + python ./autots/evaluator/benchmark.py > benchmark.txt cd @@ -448,7 +472,11 @@ python -m pip install --user --upgrade setuptools wheel cd /to project directory +# old python setup.py sdist bdist_wheel +# new +pip install --upgrade build +python -m build twine upload dist/* To use this API token: Set your username to __token__ diff --git a/tests/test_autots.py b/tests/test_autots.py index cca1d6d4..89897b15 100644 --- a/tests/test_autots.py +++ b/tests/test_autots.py @@ -474,6 +474,7 @@ def test_models(self): random_seed = 300 df = load_daily(long=False).iloc[:, 0:5] df = df[df.index < "2022-10-04"] # update dataset and have not yet updated stored model results + df = df[df.index > "2017-10-04"] # update dataset and have not yet updated stored model results models = [ 'SectionalMotif', 'MultivariateMotif', 'AverageValueNaive', 'NVAR', "LastValueNaive", 'Theta', 'FBProphet', 'SeasonalNaive', @@ -829,7 +830,7 @@ def test_sklearn(self): ) model.fit(df_train) first_forecast = model.predict(future_regressor=future_regressor_forecast) - self.assertListEqual(first_forecast.index.tolist(), df_test.index.tolist()) + self.assertListEqual(first_forecast.forecast.index.tolist(), df_test.index.tolist()) model.fit_data(df) updated_forecast = model.predict() self.assertEqual(updated_forecast.forecast.shape[0], forecast_length)

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a/docs/build/html/.buildinfo +++ b/docs/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 8bc00586ba905c50c3f982bde931d377 +config: d764de339f97f1b8b551d7c831bc8c93 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index ba0454ee..4b35c471 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.5.8', + VERSION: '0.6.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 9ca8caf5..4bbe27bc 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -5,7 +5,7 @@ - Index — AutoTS 0.5.8 documentation + Index — AutoTS 0.6.0 documentation @@ -769,10 +769,10 @@

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