Merge pull request #193 from winedarksea/dev

0.6.0
winedarksea · Aug 7, 2023 · f2a332d · f2a332d
2 parents 7c5d35c + 1cb522e
commit f2a332d
Show file tree

Hide file tree

Showing 55 changed files with 2,471 additions and 755 deletions.
diff --git 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
@@ -12,12 +12,15 @@
 * The most recent data will generally be the most important
 * Forecasts are desired for the future immediately following the most recent data.
 
-# 0.5.8 🌷🌷🌷
-* added plot_validations
-* updated pytorch forecasting for 1.0.0 version
-* plot_grid to prediction object
-* breaking change: plot_per_series_smape switched to the more accurately described plot_per_series_mape
-* various bug fixes such as 'TotalRuntime' missing
+# 0.6.0 ♨️♨️♨️
+* import_best_model and fit_data
+* added canberra distance to SeasonalityMotif and MetricMotif
+* DatepartRegressionTransformer now handles NaN in input data
+* new DatepartRegression and SeasonalityMotif based imputers
+* LevelShiftMagic transformer added
+* adjusted automatic resampling to be performed only when necessary (to avoid filling NA with 0 bug and aggfunc='sum')
+* fixed a bug where if a full validation round fails, best model selection fails
+* macro_micro to LocalLinearTrend
 
 ### Unstable Upstream Pacakges (those that are frequently broken by maintainers)
 * Pytorch-Forecasting

diff --git a/autots/__init__.py b/autots/__init__.py
@@ -26,7 +26,7 @@
 from autots.models.cassandra import Cassandra
 
 
-__version__ = '0.5.8'
+__version__ = '0.6.0'
 
 TransformTS = GeneralTransformer
 

diff --git a/autots/datasets/data/holidays.zip b/autots/datasets/data/holidays.zip
diff --git a/autots/evaluator/anomaly_detector.py b/autots/evaluator/anomaly_detector.py
@@ -319,12 +319,20 @@ def plot_anomaly(self, kwargs={}):
         self.anomaly_model.plot(**kwargs)
 
     def plot(
-        self, series_name=None, include_anomalies=True, title=None, plot_kwargs={}
+        self,
+        series_name=None,
+        include_anomalies=True,
+        title=None,
+        plot_kwargs={},
+        series=None,
     ):
         import matplotlib.pyplot as plt
 
         if series_name is None:
-            series_name = random.choice(self.df.columns)
+            if series is not None:
+                series_name = series
+            else:
+                series_name = random.choice(self.df.columns)
         if title is None:
             title = (
                 series_name[0:50]