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hey @Tyler Blume! Thanks for using MLForecast. Yes, you can pass your categorial columns as

category

type and the sklearn API of lightgbm (

LGMBRegressor

) will handle them by default. Also if you want to use the

unique_id

as a static variable, you need to create a separate column. And to use it as a categorical feature you need to convert it to

category.

Here’s an example (

unique_id

already is of type

category

):

import lightgbm as lgb
from mlforecast import MLForecast
from mlforecast.utils import generate_daily_series


series = generate_daily_series(100, equal_ends=True, n_static_features=2, static_as_categorical=True)

series['my_id_col'] = series['unique_id'].copy()

mlf = MLForecast(
    models=lgb.LGBMRegressor(),
    freq='D',
    lags=[7],
)
mlf.fit(series, static_features=['my_id_col', 'static_0', 'static_1'])

preds = mlf.predict(12)

Hey @fede (nixtla) (they/them) awesome thanks so much. Any plans to make using the id column default behavior? Using a categorical id a long with scaling at the time series level (boxcox, standard, minmax) gives nice performance boosts!

hey @Tyler Blume! Could you help us open an issue to include that behavior? https://github.com/Nixtla/mlforecast/issues/new/choose Thank you!

hey @fede (nixtla) (they/them) sorry it took so long to get around to this but I have opened a couple of issues up. I also wrote a wrapper a little while back that does a lot of what I see gives good performance boosts. It only uses LightGBM but it adds some other features that make it a little easier to tune (in my experience) I pushed it to github if interested!

This is really cool, @Tyler Blume! wdyt @José Morales? maybe we could include some of these features natively in mlforecast, would you like to contribute to that @Tyler Blume? 🎉

Yeah sounds good to me!

If you have time to open a PR that'd be great. I think you can create a new module with these features. Let us know if you need help, nbdev takes some time to getting used to