Checks if the forecasting model with this configuration supports probabilistic predictions. By default, returns False. Needs to be o

Whether model supports sample weight for training.

Whether the model supports a validation set during training.

Whether model supports sample weight for training.

Whether model supports sample weight for training.

Whether model supports static covariates

Whether the model supports prediction for any input `series`.

Whether the model supports prediction for any input `series`.

Whether the model supports prediction for any input `series`.

Whether the model supports prediction for any input `series`.

Check that consecutive calls to fit() and predict() work as intended

Check that exception is raised when fit() arguments are incorrects

Check that exception specific to FittableAggregator are properly raised

Check that predict() works as intended

datetime_attributes are 0-indexed (shift is applied when necessary)

Add regressor contains invalid component names.

Tests the add_regressor_configs parameter.

Check relative start position adjustment.

Check that exception is raised when predict() arguments are incorrects.

Check that values are properly extracted when the beginning of the series is aligned with the low frequency anchor value (day of the

Test that all default options can be accessed.

Test attention (feature importance) explanation results and plotting.

Residuals and backtest are already tested for quantile, and interval metrics based on stochastic or quantile forecasts. So, a simple check tha

Passing unsupported covariate should raise an exception

Tests backtest for classification models with different prediction methods and configurations.

When overlap_end=True and predict_likelihood_parameters=True, the last forecast window can extend beyond the series end, producing an

Test backtest method with different retrain arguments

check that passing sample weights work and that it yields different results than without sample weights.