Feat/encoder improvement

[dennisbader]

Summary

• extend encoder support to all forecasting models supporting covariates
• adds covariate lags to encoders
• encoders now generate the correct minimum required covariate time spans for all models (assuming that user did not supply any covariates). This is done with respect to parameters input_chunk_length, output_chunk_length, covariates_lags (past_covariates_lags, future_covariates_lags). Required some unit test adaptions to capture the new logic.
• improve encoder documentation including presentation
• make one shot regression models work properly with encoders

Additional improvements:

• KalmanForecaster is now a TransferrableFutureCovariatesLocalForecastingModel, meaning that predict() accepts a different target than used when fitting the model
• simplify the way Explainer handles encoders thanks to the encoder improvements.

Additional fixes:

• LocalForecastingModel slicing of integer indexed future covariates

TODO (in future PR)

• make Encoders compatible with BaseDataTransformer for pipelines
• generate encodings at beginning of historical_forecasts to increase efficiency

[codecov-commenter]

Codecov Report

Base: 93.68% // Head: 93.67% // Decreases project coverage by -0.00% ⚠️

Coverage data is based on head (3fb2e43) compared to base (4e25889).
Patch coverage: 95.62% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1338      +/-   ##
==========================================
- Coverage   93.68%   93.67%   -0.01%     
==========================================
  Files          94       94              
  Lines        9400     9485      +85     
==========================================
+ Hits         8806     8885      +79     
- Misses        594      600       +6     

Impacted Files	Coverage Δ
darts/explainability/explainability.py	96.42% <ø> (ø)
darts/explainability/shap_explainer.py	87.92% <ø> (-0.18%)	⬇️
darts/models/filtering/kalman_filter.py	98.71% <ø> (-0.02%)	⬇️
darts/timeseries.py	91.70% <ø> (-0.08%)	⬇️
darts/models/forecasting/forecasting_model.py	96.70% <92.45%> (-0.35%)	⬇️
darts/dataprocessing/encoders/encoder_base.py	96.01% <95.00%> (-0.68%)	⬇️
darts/dataprocessing/encoders/encoders.py	98.86% <100.00%> (+0.04%)	⬆️
darts/models/filtering/filtering_model.py	100.00% <100.00%> (ø)
darts/models/forecasting/arima.py	95.34% <100.00%> (ø)
darts/models/forecasting/auto_arima.py	100.00% <100.00%> (ø)
... and 13 more

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[hrzn]

Looks good, thanks for these changes @dennisbader ! Got a few small comments

[hrzn]

[Nitpicking - no need to do it if annoying] Instead of supporting all these different cases based on conventions (e.g "if calling from a RegressionModel, set the parameters in this way"), you could consider using factory methods (a bit like we have in TimeSeries). E.g. CovariatesIndexGenerator.for_regression_model(lags, lags_past_cov, lags_fut_cov, out_len) and CovariatesIndexGenerator.for_torch_model(in_len, out_len).

[hrzn]

Don't forget that slice() is inclusive on the right for DatetimeIndex

[dennisbader]

I think it is correct, but let me know if I'm missing something.
start is one step after end of target series, and we end at:

• n - 1 steps ahead of start for DatetimeIndex (as inclusive with slice)
• n steps ahead of start for RangeIndex (as non-inclusive with slice)

[hrzn]

This was not needed?

[dennisbader]

This was related to

• fixed TimeSeries.append() that dropped "time" index for integer indexed series

[hrzn]

Also not forget: shift encodings when using one shot.

[hrzn]

Thanks @dennisbader !