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[timeseries] Add scale feature and normalize covariates for MLForecast models #4069

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merged 3 commits into from
Apr 9, 2024

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shchur
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@shchur shchur commented Apr 9, 2024

Description of changes:

  • Some lightweight feature engineering for MLForecast-based models:
    • Add two static features, encoding mean & std of the target time series for each item
    • For each continuous real-valued covariate, add a copy of this covariate that is mean-abs-scaled per item

By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

@shchur shchur requested a review from canerturkmen April 9, 2024 13:44
@yinweisu
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yinweisu commented Apr 9, 2024

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LGTM!

for col in self.metadata.known_covariates_real:
# Normalize non-boolean features using mean_abs scaling
if not df[col].isin([0, 1]).all():
df[f"__scaled_{col}"] = df[col] / df[col].abs().groupby(df[ITEMID]).mean().reindex(df[ITEMID]).values
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nice pandas sorcery :)

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yinweisu commented Apr 9, 2024

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yinweisu commented Apr 9, 2024

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github-actions bot commented Apr 9, 2024

Job PR-4069-79aa307 is done.
Docs are uploaded to http://autogluon-staging.s3-website-us-west-2.amazonaws.com/PR-4069/79aa307/index.html

@shchur shchur merged commit a9948a4 into autogluon:master Apr 9, 2024
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@shchur shchur deleted the normalize-mlf-features branch April 9, 2024 19:20
LennartPurucker pushed a commit to LennartPurucker/autogluon that referenced this pull request Jun 1, 2024
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3 participants