0.3.2

Latest

• Table of Contents to Extended Tutorial/Readme.md
• Production Example
• add weights="mean"/median/min/max
• UnivariateRegression
• fix check_pickle error for ETS
• fix error in Prophet with latest version
• VisibleDeprecation warning for hidden_layers random choice in sklearn fixed
• prefill_na option added to allow quick filling of NaNs if desired (with zeroes for say, sales forecasting)
• made horizontal generalization more stable
• fixed bug in VAR where failing on data with negatives

0.3.1

Latest

• Additional models to GluonTS
• GeneralTransformer transformation_params - now handle None or empty dict
• cleaning up of the appropriately named 'ModelMonster'
• improving MotifSimulation
• better error message for all models
• enable histgradientboost regressor, left it out before thinking it wouldn't stay experimental this long
• import_template now has slightly better method input style
• allow ensemble parameter to be a list
• NumericTransformer
  • add .fit_transform method
  • generally more options and speed improvement
• added NumericTransformer to future_regressors, should now coerce if they have different dtypes

0.3.0

Latest

• breaking change to model templates: transformers structure change
  • grouping no longer used
• parameter generation for transformers allowing more possible combinations
• transformer_max_depth parameter
• Horizontal Ensembles are now much faster by only running models on the subset of series they apply to
• general starting template improved and updated to new transformer format
• change many np.random to random
  • random.choices further necessitates python 3.6 or greater
• bug fix in Detrend transformer
• bug fix in SeasonalDifference transformer
• SPL bug fix when NaN in test set
• inverse_transform now fills NaN with zero for upper/lower forecasts
• expanded model_list aliases, with dedicated module
• bug fix (creating 0,0 order) and tuning of VARMAX
• Fix export_template bug
• restructuring of some lower-level function locations

0.2.8

Latest

• Round transformer to replace coerce_integer, ClipOutliers expanded, Slice to replace context_slicer
• pd.df Interpolate methods added to FillNA options, " " to "_" in names, rolling_mean_24
• slight improvement to printed progress messages
• transformer_list (also takes a dict of value:probability) allows adjusting which transformers are created in new generations.
  • this does not apply to transformers loaded from imported templates

0.2.7

Latest

• 2x speedup in transformation runtime by removing double transformation
• joblib parallel to UnobservedComponents
• ClipOutliers transformer, Discretize Transformer, CenterLastValue - added in prep for transform template change
• bug fix on IntermittentOccurence
• minor changes to ETS, now replaces single series failure with zero fill, damped now is damped_trend
• 0.3.0 is expected to feature a breaking change to model templates in the transformation/pre-processing

0.2.6

Latest

• fix verbose > 2 error in auto_model
• use of f-strings to print some error messages. Python 3.5 may see more complicated error messages as a result.
• improved BestN (formery Best3) Ensembles, ensemble collected in dicts
• made Horizontal and BestN ensembles tolerant of a component model failure
• made Horizontal models capable of generalizing from a subset of series
• added info to model table for models that can use future_regressor
• added Datepart Regression model, sklearn regressor on time components only

0.2.5

Latest

• fix error where wide data import skipped cleaning steps
• long=True/False for all example data

0.2.4

Latest

• ARIMA to ARIMAX (for Statsmodels v0.13)
• ARIMA parallelization
• update of daily sample data, reduced space used by yearly and hourly
• n_jobs = 'auto'
• models table to extended_tutorial.md

0.2.3

Latest

• Added n_jobs parameters to pass through to joblib (although a joblib context manager is perhaps the best way)
• Added joblib multiprocessing to ETS, GLM, and FBProphet
• Fixed future warnings with pandas.DatetimeIndex.week and changes to statsmodels ETS
• standardized source code formatting

0.2.2

Latest:

• grouping/hierarchial reconciliation to GeneralTransformer
• allow wide-style data as input
• iterative imputer
• allow a list of intervals to prediction_intervals in .predict()