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Systematic review highlights fragmentation in time series XAI frameworks

By PulseAugur Editorial · [1 sources] ·

A new systematic review published on arXiv analyzes software frameworks designed for explainable AI (XAI) in time series classification. The review identifies six frameworks that specifically support time series data, but highlights significant limitations across them. These include a lack of support for frequency-domain explanations, a scarcity of time-series-specific evaluation metrics, and inconsistencies in explanations generated by the same XAI methods across different frameworks. The authors call for the development of unified, time-series-aware XAI frameworks to improve faithfulness, reproducibility, and practical utility. AI

IMPACT Identifies critical gaps in current XAI tools for time series data, potentially guiding future development for more reliable and reproducible AI systems.

RANK_REASON Academic paper detailing a systematic review of software frameworks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Systematic review highlights fragmentation in time series XAI frameworks

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Academic paper detailing a systematic review of software frameworks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Louis Peter, Nils Gumpfer, Jana Fischer, Christin Seifert, Jennifer Hannig ·

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

    arXiv:2608.21449v1 Announce Type: new 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, …

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