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

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

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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, Sep 13, 2021 - Sep 17, 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, pp. 5-14. ISBN 978-3-030-93735-5. Available under: doi: 10.1007/978-3-030-93736-2_1

@inproceedings{Schlegel2022TSMUL-55037, title={TS-MULE : Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models}, year={2022}, doi={10.1007/978-3-030-93736-2_1}, number={1524}, isbn={978-3-030-93735-5}, address={Cham}, publisher={Springer}, 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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