Published June 13, 2022
| Version v2
Dataset
Open
Towards understanding the importance of time-series features in automated algorithm performance prediction
Description
merged_feature_importance.csv - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.
Catch22.csv - Catch22 features (raw time-series)
Catch22Log.csv - Catch22 features (log time-series)
Catch22Diff.csv - Catch22 features (differenced time-series)
TSFresh.csv - TSFresh features (raw time-series)
TSFreshLog.csv - TSFresh features (log time-series)
TSFreshDiff.csv - TSFresh features (differenced time-series)
mape.csv - sMAPE performance for all forecasting algoirthms
Files
Catch22.csv
Files
(3.4 GB)
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md5:b6cf20d3bf439dc316ecbac566c5f132
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md5:13a077a222ea3d78217f632ea9c94db3
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md5:10a0482e20bf6928588b76c42090af00
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32.3 MB | Preview Download |
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md5:2dc06b16d9ea364985d66637b2654f3d
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122.8 MB | Preview Download |
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md5:711b06dc67ae5b8f421f973cdbe10404
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39.0 MB | Preview Download |
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md5:4b1e24783efb9ebad1dc8015a577732c
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1.0 GB | Preview Download |
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md5:ab69f2beb55f801b326da18806ce7f70
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1.0 GB | Preview Download |
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md5:6317f50c9ef0123acac63b9853b466ca
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1.1 GB | Preview Download |