Allowing Application of Pipelines for Historical Forecasts and Backtests in Darts

[Nicholas-Amirsoleimani]

Is your feature request related to a current problem? Please describe.
Yes, currently in Darts, the historical_forecasts and backtest methods do not provide an option to apply the inverse_transform method of a pipeline for each historical forecast. This is particularly problematic for transformers like darts.dataprocessing.transformers.Diff that rely on previous training values. Since for these transformers, we cannot simply apply inverse_transform to all historical forecasts at once. It requires the knowledge of the training data known at each time point rather than just the training data at the end of the original training set.

Describe proposed solution
A solution to this problem could be to allow an optional pipeline parameter in the historical_forecasts and backtest methods that, if provided, applies the inverse_transform method of the pipeline to each individual historical forecast. This would allow each forecast to be made with only the knowledge of the training data up to that point in time. The pipeline parameter could be optional, and if not provided, the methods would work as they currently do.

Describe potential alternatives
An alternative could be to create a new transformer that handles this specific situation, i.e., a transformer that, when provided with a pipeline, would automatically apply the inverse_transform method of the pipeline after each individual forecast. However, this might not be as flexible or as easily understandable as the proposed solution.

Additional context
This feature would be particularly beneficial for situations where the preprocessing step involves a transformation that relies on previous training values (like differencing). Currently, the inverse of this transformation cannot be correctly applied to the historical forecasts, which can lead to incorrect or misleading results. By allowing the inverse_transform method of a pipeline to be applied after each forecast, it would ensure more accurate results in these situations.

[madtoinou]

Hi @Nicholas-Amirsoleimani,

I think that this is a duplicate of #1540 (with a slightly different application/rational), feel free to reopen it if you feel like it's a different feature and should get a particular attention.