Comments (2)
Djuseppe
commented on May 27, 2024
1
thanks for the info 🙂
indeed a custom training is amazing and suits me nicely, since I have also custom CV with Optuna optimization.
Closing, as existing features are just enough.
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jmoralez
commented on May 27, 2024
Hey @Djuseppe, thanks for using mlforecast. We have that built-in for LightGBM in the LightGBMCV class and you can do it for other frameworks by using custom training. It'd be very hard to have a general solution because each framework has a slightly different interface.
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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: can the number of exogenous variables be different for different unique_id? 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
- 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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