Scalecast

Added
- added probabilistic forecasting through `Forecaster.proba_forecast()` and `MVForecaster.proba_forecast()` methods
- added level confidence intervals always for models run at level (so it won't fail to generate cis anymore when passing `Forecaster.plot(ci=True,level=True)`)
- probabilistic forecasting also makes it possible to derive level confidence intervals even when model was run at difference
- added probabilistic as a `bool` argument to `Forecaster.tune_test_forecast()`, `MVForecaster.tune_test_forecast()`, and `notebook.tune_test_forecast()` functions
Changed
- changed how it was determined that a model was tuned for efficiency gains
- changed so that "Dynamically" does not appear in the history['tuned'] attribute
- changed the error that is raised when reduce_Xvars() doesn't work due to feature importance not being supported by a given model so that it is more explicit
- changed the default mlp grid so that random_state is no longer a value. this makes that model more ammenable to probabilistic forecasting
Fixed

Added
- added `suffix` argument to `Forecaster.tune_test_forecast()`, `MVForecaster.tune_test_forecast()`, and `notebook.tune_test_forecast()` functions (5)
- added `fi_method` argument to `notebook.tune_test_forecast()` function
Changed
Fixed

Added
- added `AnomalyDetector.MonteCarloDetect_sliding()` method
Changed
- changed the `dynamic_testing` and `dynamic_tuning` arguments in `Forecaster` and `MVForecaster` so that window forecast evaluation is now supported. now, instead of having the choice between 1-step and arbitrary multi-step forecasting, any integer value is accepted as arguments in those parameters but `True` and `False` are still supported and do the same thing as always.
Fixed

Added
Changed
- got rid of printing when calling `ChangepointDetector.WriteCPtoXvars()`
Fixed
- fixed cross validation when `Xvars = 'all'` is passed as an argument

Added
- added the `ChangepointDetector` object
- added the `AnomalyDetector.adjust_anom()` method
Changed
Fixed
- fixed last index span from `AnomalyDetector.MonteCarloDetect()`

Added
- added `jump_back` parameter in `Forecaster.backtest()` and `MVForecaster.backtest()` methods
- added the theta model from darts
Changed
- changed how dataframes are grouped in the `cross_validate()` method. turned sorting off to prevent some failures, specifically in the theta model
Fixed