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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2021

AUTHORS

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

ABSTRACT

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. More... »

PAGES

5-14

Book

TITLE

Machine Learning and Principles and Practice of Knowledge Discovery in Databases

ISBN

978-3-030-93735-5
978-3-030-93736-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-93736-2_1

DOI

http://dx.doi.org/10.1007/978-3-030-93736-2_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1145662948


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