LIMESegment: Meaningful, Realistic Time Series Explanations

Torty Sivill, Peter Flach
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:3418-3433, 2022.

Abstract

LIME (Locally Interpretable Model-Agnostic Explanations) has become a popular way of generating explanations for tabular, image and natural language models, providing insight into why an instance was given a particular classification. In this paper we adapt LIME to time series classification, an under-explored area with existing approaches failing to account for the structure of this kind of data. We frame the non-trivial challenge of adapting LIME to time series classification as the following open questions: “What is a meaningful interpretable representation of a time series?”, “How does one realistically perturb a time series?” and “What is a local neighbourhood around a time series?”. We propose solutions to all three questions and combine them into a novel time series explanation framework called LIMESegment, which outperforms existing adaptations of LIME to time series on a variety of classification tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-sivill22a, title = { LIMESegment: Meaningful, Realistic Time Series Explanations }, author = {Sivill, Torty and Flach, Peter}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {3418--3433}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/sivill22a/sivill22a.pdf}, url = {https://proceedings.mlr.press/v151/sivill22a.html}, abstract = { LIME (Locally Interpretable Model-Agnostic Explanations) has become a popular way of generating explanations for tabular, image and natural language models, providing insight into why an instance was given a particular classification. In this paper we adapt LIME to time series classification, an under-explored area with existing approaches failing to account for the structure of this kind of data. We frame the non-trivial challenge of adapting LIME to time series classification as the following open questions: “What is a meaningful interpretable representation of a time series?”, “How does one realistically perturb a time series?” and “What is a local neighbourhood around a time series?”. We propose solutions to all three questions and combine them into a novel time series explanation framework called LIMESegment, which outperforms existing adaptations of LIME to time series on a variety of classification tasks. } }
Endnote
%0 Conference Paper %T LIMESegment: Meaningful, Realistic Time Series Explanations %A Torty Sivill %A Peter Flach %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-sivill22a %I PMLR %P 3418--3433 %U https://proceedings.mlr.press/v151/sivill22a.html %V 151 %X LIME (Locally Interpretable Model-Agnostic Explanations) has become a popular way of generating explanations for tabular, image and natural language models, providing insight into why an instance was given a particular classification. In this paper we adapt LIME to time series classification, an under-explored area with existing approaches failing to account for the structure of this kind of data. We frame the non-trivial challenge of adapting LIME to time series classification as the following open questions: “What is a meaningful interpretable representation of a time series?”, “How does one realistically perturb a time series?” and “What is a local neighbourhood around a time series?”. We propose solutions to all three questions and combine them into a novel time series explanation framework called LIMESegment, which outperforms existing adaptations of LIME to time series on a variety of classification tasks.
APA
Sivill, T. & Flach, P.. (2022). LIMESegment: Meaningful, Realistic Time Series Explanations . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:3418-3433 Available from https://proceedings.mlr.press/v151/sivill22a.html.

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