Customize the training procedure for your models
MLForecast.preprocess
to generate the training data.
unique_id | ds | y | lag1 | dayofweek | |
---|---|---|---|---|---|
1 | id_0 | 2000-01-02 | 1.423626 | 0.428973 | 6 |
2 | id_0 | 2000-01-03 | 2.311782 | 1.423626 | 0 |
3 | id_0 | 2000-01-04 | 3.192191 | 2.311782 | 1 |
4 | id_0 | 2000-01-05 | 4.148767 | 3.192191 | 2 |
5 | id_0 | 2000-01-06 | 5.028356 | 4.148767 | 3 |
MLForecast.fit_models
MLForecast.models_
attribute.
MLForecast.models_
dictionary.
Note that you can assign as many models as you want.
MLForecast.predict
,
mlforecast will use those models to compute the forecasts.
unique_id | ds | lr | lgbm | |
---|---|---|---|---|
0 | id_0 | 2000-08-10 | 3.549124 | 5.166797 |
1 | id_1 | 2000-04-07 | 3.154285 | 4.252490 |
2 | id_2 | 2000-06-16 | 2.880933 | 3.224506 |
3 | id_3 | 2000-08-30 | 4.061801 | 0.245443 |
4 | id_4 | 2001-01-08 | 2.904872 | 2.225106 |