Software Frameworks for Explainable AI in Time Series Classification: A Systematic Review

• URL: http://arxiv.org/abs/2608.21449v1
• Date: Wed, 19 Aug 2026 15:24:17 GMT
• Title: Software Frameworks for Explainable AI in Time Series Classification: A Systematic Review
• Abstract summary: Time series classification (TSC) is one of the most widely studied and relevant tasks.<n> ensuring the transparency and trustworthiness of TSC models has become an important requirement.<n>Despite growing interest, research on XAI for TSC remains fragmented.
• Score: 4.913695293147755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series arise in a wide range of application domains and are analyzed using machine learning in decision-critical settings. Time series classification (TSC) is one of the most widely studied and relevant tasks. In this context, ensuring the transparency and trustworthiness of TSC models has become an important requirement, motivating the use of explainable artificial intelligence (XAI) methods. Despite growing interest, research on XAI for TSC remains fragmented, and a systematic understanding of the available software frameworks for explanation generation, their evaluation practices, and practical limitations is still lacking. Prior work largely focused on individual explanation methods, while cross-framework consistency, time-series-specific evaluation, and reproducibility have received little attention. In this survey, we analyze existing software frameworks for explanation generation and evaluation in TSC. We compare them along multiple dimensions, including supported XAI methods, evaluation metrics, usability, benchmarking support, and reproducibility, providing the first time-series-specific survey of frameworks with implementation comparisons and an analysis of frequency-domain support. We identify six frameworks that explicitly support time series and reveal common limitations: only one method supports frequency-domain explanations despite their relevance; only two evaluation metrics have been developed specifically for time series; and identical XAI methods can yield substantially different explanations across frameworks. Based on these findings, we discuss open challenges and outline directions for future research, highlighting the need for unified, time-series-specific XAI frameworks that enable faithful, reproducible, and time-series-aware explanations.

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