Autots

Latest :space_invader: :space_invader: :space_invader:
* plot_horizontal_per_generation and horizontal_per_generation added
* model_list now accepts a dictionary of probabilities, however this only affects new Random Templates
* :seedling: improved the genetic algorithm for new model generation
* minor improvement to generate_score_per_series for handling very small ~e-20 errors
* added PytorchForecasting to available models
* Johansen Cointegration transformer
* BTCD transformer
* Johansen and BTCD as Regression features
* fixed bug in plot_horizontal()
* added ARCH to available models
* changed the sklearn models used by UnivariateRegression by default and returned to default model_list
* fixed a bug in KerasRNN

Latest :fireworks: :fireworks:
* Replaced ZeroesNaive model with ConstantNaive
* updated the General starting template
* added 'window' to AverageValueNaive
* added :octocat: load_artificial sample dataset
* fixed bug in plot_horizontal where not handling negative series
* major update to Constraint functionality
* GluonTS is no longer part of the default model list (faster tests this way) but is now part of 'best'
* horizontal models_to_use for Mosaic ensembles
* added some intel optimizations to sklearn code if scikit-learn-intelex installed
* fixed a :bug: in rolling_x_regressor where datepart method wasn't actually getting appended
* sped up rolling_x_regressor by reducing concats
* added current_model_file as an option for additional debugging information
* updated PCA and FastICA to be more flexible on n_components
* fixed a bug where unpack_ensemble_models with keep_ensemble=False was still keeping nested ensembles
* impute speed optimizations for fill_mean, use of nan check so runs faster if no nan present
* optimizations to metrics including faster if no NaN present
* :heavy_plus_sign: faster percentile function, used in transforms, basics
* also added nan_checks to switch between numpy na and numpy non-na quantiles/medians
* made fake_date_fill nan method vectorized
* slightly adjusted the upper/lower forecast method for LastValueNaive :crystal_ball:
* updated sliding_window_view to allow Motifs to run on Numpy < 1.20 and also for faster WindowRegression
* updated regressor_used and used_regressor_check so they should be more reliably filled.
* changed behavior where import_templates would fail if model_list not satisified. Still fails, but now only for the "only" import option
* added :chart_with_upwards_trend: when a model in validation is the best so far that round
* addition of "auto" and "max" num_validations with auto set as default
* addition of new metrics: mqae, oda, maxe

Latest
* Note: the plan is to replace ZeroesNaive model with ConstantNaive in a future release
* fix bug where score was failing to generate if made_weighting > 0 and forecast_length = 1
* made MADE functional on forecast_length=1 if df_train is provided
* SMAPE now in __repr__ of fit AutoTS class
* contour now works on forecast_length = 1
* Added NeuralProphet model
* made the probabilistic modeling of MultivariateRegression a parameter which only occurs when 'deep' mode is active (too slow)
* added more params to pass through to Prophet
* add phi damping to Detrend transformer
* add window slice to Detrend transformer
* added 'simple_2" datepart method
* added package to conda-forge
* preclean method added to AutoTS
* added median and midhinge point_methods to BestN Ensembles
* added additional model selections to 'simple' and 'subsample' ensemble
* switch LightGBM back to MultiOutputRegressor from RegressorChain for speed (with LightGBMRegressorChain replacing)
* removed top-level datasets `requests` dependency
* Added EWMAFilter
* improved NaN in forecast check
* added fail_on_forecast_nan (bool) to AutoTS.predict and model_forecast, if False, can now allow forecasts with NaN to be returned
* add return_model to model_forecast for model and transformer
* fixed bug where Detrend failing with non-datetime index
* improved error handling in Transformers to explicitly reference which failed
* added random.seed() setting in AutoTS which actually seems to standardize the runs
* sped up assembling/concat of horizontal ensembles for large numbers of series
* added polynomial_degree to date_part (and ~Transformer and ~Regression)
* updated infer_frequency and utilized in model base class
* added additional datasets (analytics.gov and severe weather) to load_lively_daily and modified pytrends load
* added rps to metrics (although no plans to build it into evaluation)
* added 'Ridge' and 'GaussianProcessRegressor' as model options for Regressions
* enforcing consistency on inner n_jobs with MultiOutputRegressor
* add DynamicFactorMQ model from Statsmodels
* added plot_per_series_smape and list_failed_model_types to output more run information from AutoTS class
* increased number of best per series models added to models to validate (models to validate has become more of a baseline than a firm number)
* finally transitioned `ensemble` parameter fully to a list from the original comma-sep string list
* MLE and iMLE logarithmic metrics for targeting under- and over- estimation
* MAGE metric for error on rollup forecasts
* Mosaic ensembles now include a metric_weighting variation including MAE, RMSE and SPL weighting (abs error, square error, pl error) (unscaled)
* minor but noticeable speedups to TemplateWizard and inferred_normal functions
* added EventRiskForecast for determing risk of exceeding limits (should be considered in beta for now)
* back_forecast now has tail/eval_periods configured
* changed behavior of import_template, by default simple ensembles are unpacked but no longer included in template unless include_ensemble is True.

Latest
* add MADE error metric (consider this beta, it may change)
* "end_generation" option to model_interrupt
* statsmodels warning adjustment (warnings now print at verbose = 2)
* add modifier to cpu_count and use with model_forecast auto_model

Latest
* added Benchmark function
* made Prophet a bit more robust for joblib
* motifsimulation bug fix
* simulation forecasting mode
* deep param mode (not yet utilized widely)

Latest
* BestN ensembles now support weighting model components
* cluster-based and generate_score_per_series-based 'simple' ensembles
* 'univariate' model_list added
* similarity and custom cross validation now set initial evaluation segment
* validation_test_indexes and train now include initial eval segment
* 'subsample' ensemble expansion of 'simple'
* added Theta model from statsmodels
* added ARDL model from statsmodels
* expanded UnobservedComponents functionality, although it still fails on some params for unknown reasons
* fixed bug in AutoTS.predict() where it was breaking regressors in some cases
* transition from [] to None as default for no future_regressor
* enforce more extensive failing if regression_type==User and no regressor passed
* fixed regressor handling in DatepartRegression