research-article

Explainable AI for time series via Virtual Inspection Layers

Authors:

Johanna Vielhaben,

Sebastian Lapuschkin,

Grégoire Montavon,

Wojciech SamekAuthors Info & Claims

Volume 150, Issue C

https://doi.org/10.1016/j.patcog.2024.110309

Published: 02 July 2024 Publication History

Abstract

The field of eXplainable Artificial Intelligence (XAI) has witnessed significant advancements in recent years. However, the majority of progress has been concentrated in the domains of computer vision and natural language processing. For time series data, where the input itself is often not interpretable, dedicated XAI research is scarce. In this work, we put forward a virtual inspection layer for transforming the time series to an interpretable representation and allows to propagate relevance attributions to this representation via local XAI methods. In this way, we extend the applicability of XAI methods to domains (e.g. speech) where the input is only interpretable after a transformation. In this work, we focus on the Fourier Transform which, is prominently applied in the preprocessing of time series, with Layer-wise Relevance Propagation (LRP) and refer to our method as DFT-LRP. We demonstrate the usefulness of DFT-LRP in various time series classification settings like audio and medical data. We showcase how DFT-LRP reveals differences in the classification strategies of models trained in different domains (e.g., time vs. frequency domain) or helps to discover how models act on spurious correlations in the data.

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Highlights

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We propose a new form of explanation for models trained on time series data.

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We derive a closed-form formula for relevance propagation through the DFT and STDFT.

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We extend evaluations to allow for comparison of explanations in different formats.

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We show how our method gives insights into strategies of audio and ECG classifiers.

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cover image Pattern Recognition

Pattern Recognition Volume 150, Issue C

Jun 2024

726 pages

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Elsevier Science Inc.

United States

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Published: 02 July 2024

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