Autots

Latest
* update validation template creation for horizontal ensembles
* made MultivariateRegression probabilistic
* fixed bug where weighting didn't take floats
* pushed the evaluate options from a separate function to part of the PredictionObject
* added 'custom' validation option
* added "similarity" validation option
* SectionalMotif model added
* window functions grouped in module
* fixed bugs in holiday_flag
* holiday_flag now has holiday categorical encoding option and works better on sub-daily data
* create_regressor handle categorical features
* 'superfast' transformer_dict now adjusts fillna methods as well
* optimizing metric calculation runtimes (feel the speed of 500 µs savings!)

Latest
* add Transformer model to sklearn DNN models
* expanded and tuned KerasRNN model options
* added param space for RandomForest, ExtraTrees, Poisson, and RANSAC regressions
* removed Tensorflow models from UnivariateRegression as it can cause a crash with GPU training
* added create_regressor function
* two new impute methods (KNNImputer, IterativeImputerExtraTrees), but only with "all" transformers
* deletion of old TSFresh model, which was horribly slow and not going to get any faster
* optimizing scalability by tuning transformer and imputation defaults
* MultivariateRegression model (RollingRegression but 1d to models)
* fix for generate_score_per_series bug with all zeroes series
* bug fix for where horizontal ensembles failed if series_ids/column names were integers

Latest
* bug fix in fake_date imputation
* bug fix in Round
* make SinTrend fail if it fails on all series (may revert this)
* load_linear and load_sine artificial datasets
* new NVAR model based on https://github.com/quantinfo/ng-rc-paper-code/
* tuning retrieve_regressor to allow it to better work with multioutput and univariate
* expand GluonTS models included
* GluonTS now works on univariate inputs
* GluonTS now works with regressors
* fixed bug where model_count wrong for mosaic ensembles
* fixed bug in VECM that meant it didn't couldn't utilize future_regressor

Latest
* back_forecast for forecast on training data
* Mosaic ensembles can now be used beyond training forecast_length and for shorter lengths too
* best_model_name, best_model_params, and best_model_transformation_params AutoTS attributes now available
* mean, median, and ffill NaN now handle fully NaN series by returning 0.
* fixed bug that was causing mosaic generalization to fail if ffill/bfill handled all missing values
* STLFilter and HPFilter and convolution_filter Transformers added

Latest
* New Transfromer ScipyFilter
* New models Univariate and MultivariateMotif
* 'midhinge' and "weighted_mean" to AverageValueNaive
* Add passing regressors to WindowRegression and made more efficient window generation
* more plotting methods: plot_horizontal_transformers
* for most -Regression type models, `model_params` is now treated as kwargs and can accept any args for that model
* ExtraTrees and RadiusRegressor to -Regression type models
* bug fix in generate_score_per_series
* 'Generation' now tracked in results table, plus plotting method for generation loss

Latest
* improvements to joblib parallelized models (not copying the full df)
* additonal parameter checks
* made "auto" cpu_count even more conservative
* improved 'Score' generation. It should now be more equally weighted across metrics.
* fixed potential bug for horizontal ensemble selection if perfect forecasts were delivered
* Horizontal ensembles now chosen by combination of multiple metrics and metric_weighting (mae, rmse, spl, contour)
* re-weighted fillna probabilities for random choice
* addressed a few deprecation warnings
* new plot_horizontal() function for AutoTS to quickly visual horizontal ensembles
* Probabilistic and HDist ensembles are now deprecated (they can still be run by model_forecast but not by AutoTS class)
* new introduce_na parameter which makes series more robust to the last values being NaN in final but never in any validation
* Mosaic Ensembles! These can offer major improvements to MAE, but are also less stable than horizontal ensembles.