Comments (2)
Hey @ahbon123, thanks for using mlforecast. The models are trained using all of the series, so it depends on what you want to do, you could for example fill the missing features with NaNs and let the model handle them or split them and model them separately.
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Related Issues (20)
- [MLForecast] lag_transforms with different features packages HOT 6
- MLForecast: Core: Add prediction intervals to forecast_fitted_values
- [Core] Saving of the model HOT 20
- [core]Using multiple models can cause `new_x` lag feature shift HOT 2
- [Core]`ts.update` method may not support target_transforms,lag_transforms and date_features HOT 1
- [Model] Distributed version of the model giving Arrow Capacity error HOT 6
- predict() and cross_validation() outputs inconsistency HOT 6
- Dynamic features for training HOT 1
- model.predict encounters an error HOT 3
- Does mlforecast train a single global model or one model per serie? HOT 2
- MLforecast does not work with with PyArrow dates HOT 2
- Fcst.predict does not accept X_df with dynamic exogenous variables HOT 2
- [Model] Distributed and Non-distributed version of the models giving different result HOT 8
- [MLForecast: test set eval and early stopping ] HOT 2
- AutoDifferences, AutoSeasonalityAndDifferences result in "AttributeError: 'NoneType' object has no attribute 'AutoDifferences'" HOT 2
- Lag feature: how initial values are treated or populated once the data has been shifted? HOT 2
- [date features]: dayofweek_cat - day of week as a one hot encoding feature HOT 4
- [Core] getting an error module 'coreforecast.lag_transforms' has no attribute 'BaseLagTransform' HOT 3
- [distributed]: allow for .ts.update in DistributedMLForecast HOT 3
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