Autots

Latest
* Fixed horizontal ensembles running in univariate cases (they are explicitly multivariate)
* 'superfast' transformer list added
* test on Mac for the first time, everything seems to work except lightgbm
* include first actual unittests (from existing test.py runs)
* slight change to random template generation to make sure all models are choosen at least once
* cleaned up PredictWitch -> model_forecast() a bit so that users can use it to run single models from parameters directly
* added load_live_daily() example data and spruced up production_example.py
* tried in vain to make a quiet verbosity option for GluonTS
* added create_lagged_regressor
* added Greykite model (additional regressors not working yet)
* fixed regressors bug in Prophet
* added a simple plot method to PredictionObject
* fix for deprecation warning in GLS

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

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

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

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

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