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Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

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arxiv 2104.00950 v1 pith:ATAACLCF submitted 2021-04-02 cs.LG cs.AI

Thomas Rojat , Raphaël Puget , David Filliat , Javier Del Ser , Rodolphe Gelin , Natalia Díaz-Rodríguez This is my paper · ORCID
classification cs.LGcs.AI
keywords methodsappliedseriestimeexplainablefieldapplicationsartificial
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Most of state of the art methods applied on time series consist of deep learning methods that are too complex to be interpreted. This lack of interpretability is a major drawback, as several applications in the real world are critical tasks, such as the medical field or the autonomous driving field. The explainability of models applied on time series has not gather much attention compared to the computer vision or the natural language processing fields. In this paper, we present an overview of existing explainable AI (XAI) methods applied on time series and illustrate the type of explanations they produce. We also provide a reflection on the impact of these explanation methods to provide confidence and trust in the AI systems.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 118 citations worldwide. Full citation record

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  5. CRITS: Convolutional Rectifier for Interpretable Time Series Classification

    cs.LG 2025-05 conditional novelty 5.0 of 10

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    On two French groundwater time series, attention-based replay sampling looks more structured and interpretable than random or loss-based alternatives, but the paper reports no numeric metrics to support its performanc...

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