TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Schlegel, Udo; Vo, Duy Lam; Keim, Daniel A.; Seebacher, Daniel

TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Dateien

Schlegel_2-vxq3bhqko6i12.pdfGröße: 663.51 KBDownloads: 32

Datum

2022

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

KAMP, Michael, ed. and others. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I. Cham: Springer, 2022, pp. 5-14. Communications in Computer and Information Science. 1524. ISBN 978-3-030-93735-5. Available under: doi: 10.1007/978-3-030-93736-2_1

Zusammenfassung

Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and debugging are not guaranteed. We propose TS-MULE, a local surrogate model explanation method specialized for time series extending the LIME approach. Our extended LIME works with various ways to segment and perturb the time series data. In our extension, we present six sampling segmentation approaches for time series to improve the quality of surrogate attributions and demonstrate their performances on three deep learning model architectures and three common multivariate time series datasets.

Fachgebiet (DDC)

004 Informatik

Konferenz

Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, 13. Sep. 2021 - 17. Sep. 2021

Zitieren

ISO 690

SCHLEGEL, Udo, Duy Lam VO, Daniel A. KEIM, Daniel SEEBACHER, 2022. TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, 13. Sep. 2021 - 17. Sep. 2021. In: KAMP, Michael, ed. and others. Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I. Cham: Springer, 2022, pp. 5-14. Communications in Computer and Information Science. 1524. ISBN 978-3-030-93735-5. Available under: doi: 10.1007/978-3-030-93736-2_1

BibTex

@inproceedings{Schlegel2022TSMUL-55037,
  year={2022},
  doi={10.1007/978-3-030-93736-2_1},
  title={TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models},
  number={1524},
  isbn={978-3-030-93735-5},
  publisher={Springer},
  address={Cham},
  series={Communications in Computer and Information Science},
  booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Proceedings, Part I},
  pages={5--14},
  editor={Kamp, Michael},
  author={Schlegel, Udo and Vo, Duy Lam and Keim, Daniel A. and Seebacher, Daniel}
}

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Universitätsbibliographie

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