Tune your forecasting models
unique_id | ds | lgb | ridge | |
---|---|---|---|---|
0 | H1 | 701 | 680.534943 | 604.140123 |
1 | H1 | 702 | 599.038307 | 523.364874 |
2 | H1 | 703 | 572.808421 | 479.174481 |
3 | H1 | 704 | 564.573783 | 444.540062 |
4 | H1 | 705 | 543.046026 | 419.987657 |
lgb | ridge | |
---|---|---|
SMAPE | 18.78 | 20.00 |
MASE | 5.07 | 1.29 |
OWA | 1.57 | 0.81 |
my_lgb | |
---|---|
SMAPE | 18.67 |
MASE | 4.79 |
OWA | 1.51 |
ridge | |
---|---|
SMAPE | 18.50 |
MASE | 1.24 |
OWA | 0.76 |
MLForecast
class defines the features to build in its constructor. You can tune the
features by providing a function through the init_config
argument,
which will take an optuna trial and produce a configuration to pass to
the
MLForecast
constructor.
AutoRidge | |
---|---|
SMAPE | 13.31 |
MASE | 1.67 |
OWA | 0.71 |
MLForecast.fit
method takes some arguments that could improve the forecasting
performance of your models, such as dropna
and static_features
. If
you want to tune those you can provide a function to the fit_config
argument.
AutoLightGBM | |
---|---|
SMAPE | 18.78 |
MASE | 5.07 |
OWA | 1.57 |
results_
attribute of the
AutoMLForecast
object. There will be one result per model and the best configuration
can be found under the config
user attribute.
MLForecast
objects and are saved in the models_
attribute.
AutoMLForecast.save
method to save the best models found. This produces one directory per
model.
MLForecast
object you can load it by itself.