[BUG] multi_models=FALSE not working for XGBOOST about darts

[madtoinou]

Hi @suswamin,

Sorry for the delay, I did not realize that this issue went without answer for so long.

When a model is created with multi_models=False, only one set of coefficients is fitted for all the forecasted positions. Since using the same features with one set of coefficient would yield the exact same output for all the position in output_chunk_length, the lags are actually shifted in the past by output_chunk_length - 1.

You can visualize how the lags (target, past_covariates and future_covariates) are generated in the regression example notebook.

This means that to forecast the 7 values after the end of the training series, the length requirements when multi_models=False/True are different.

validation_start = len(data)-8
shift = 7 - 1
forecast_validation = data.iloc[validation_start - shift:validation_start + 1]['weekday']
# using model created with `multi_models=False`
pred_xgb = XGB.predict(n=7, future_covariates=darts.TimeSeries.from_series(forecast_validation))