breaking: add `hist_exog_list` argument to forecast

[jmoralez]

Adds the hist_exog_list argument to NixtlaClient.forecast so users can indicate which features they want to treat as historic. The previous behavior was to treat all features in df that weren't in X_df as historic, this now requires that decision to be explicit, so the proposed behavior is:

• If X_df isn't specified and df has exogenous features, they are ignored.
• If X_df was provided but it doesn't have all the features in df and hist_exog is not provided, only the features from X_df are used, the rest are ignored.
• If X_df was provided but it doesn't have all the features in df and hist_exog is provided, the features from X_df and the hist_exog features are used, the rest (if any) are ignored.

When features are ignored a warning is issued. The used features, both historic and future, are logged.

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Experiment Results

## Experiment 1: air-passengers ### Description: | variable | experiment | |:--------------|:-------------| | h | 12 | | season_length | 12 | | freq | MS | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|-----------:| | mae | 12.6793 | 11.0623 | 47.8333 | 76 | | mape | 0.027 | 0.0232 | 0.0999 | 0.1425 | | mse | 213.936 | 199.132 | 2571.33 | 10604.2 | | total_time | 1.1348 | 2.096 | 0.0056 | 0.004 | ### Plot:  ## Experiment 2: air-passengers ### Description: | variable | experiment | |:--------------|:-------------| | h | 24 | | season_length | 12 | | freq | MS | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|-----------:| | mae | 58.1031 | 58.4587 | 71.25 | 115.25 | | mape | 0.1257 | 0.1267 | 0.1552 | 0.2358 | | mse | 4040.21 | 4110.79 | 5928.17 | 18859.2 | | total_time | 0.9037 | 1.339 | 0.0045 | 0.0039 | ### Plot:  ## Experiment 3: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 24 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|---------------:| | mae | 178.293 | 268.13 | 269.23 | 1331.02 | | mape | 0.0234 | 0.0311 | 0.0304 | 0.1692 | | mse | 121589 | 219485 | 213677 | 4.68961e+06 | | total_time | 1.3647 | 3.318 | 0.0053 | 0.0052 | ### Plot:  ## Experiment 4: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 168 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|---------------:| | mae | 465.497 | 346.976 | 398.956 | 1119.26 | | mape | 0.062 | 0.0436 | 0.0512 | 0.1583 | | mse | 835021 | 403762 | 656723 | 3.17316e+06 | | total_time | 0.9588 | 1.9855 | 0.0057 | 0.0051 | ### Plot:  ## Experiment 5: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 336 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|--------------:|-------------------------:|----------------:|---------------:| | mae | 558.673 | 459.757 | 602.926 | 1340.95 | | mape | 0.0697 | 0.0565 | 0.0787 | 0.17 | | mse | 1.22723e+06 | 739114 | 1.61572e+06 | 6.04619e+06 | | total_time | 1.5547 | 0.9623 | 0.0059 | 0.0053 | ### Plot: