Review of XAI Frameworks for Time Series Classification
Key takeaways
- Current XAI frameworks for time series classification have significant limitations, including poor frequency-domain support.
- Identical XAI methods can yield inconsistent explanations across different software frameworks.
- There is a critical need for unified, time-series-specific XAI frameworks that ensure reproducibility and faithfulness.
- Professionals must be aware of these limitations when deploying XAI in decision-critical time series applications.
Who benefits
Summary
This systematic review analyzes existing software frameworks for Explainable AI (XAI) in time series classification (TSC), comparing their methods, evaluation metrics, and limitations. It identifies challenges like limited frequency-domain support and inconsistencies across frameworks for identical XAI methods.
Why it matters
Professionals deploying AI in critical time-series applications need reliable and consistent explanations for model decisions, and this review highlights current gaps and inconsistencies in available XAI tools. Understanding these limitations is crucial for selecting appropriate tools and advocating for better framework development.
How to implement this in your domain
- 1Evaluate current XAI tools for time series applications against the identified limitations, especially regarding frequency-domain support and evaluation metrics.
- 2Prioritize XAI frameworks that demonstrate strong reproducibility and consistent explanation generation for critical time series models.
- 3Contribute to or advocate for the development of unified, time-series-specific XAI frameworks that address current gaps.
- 4Develop internal guidelines for evaluating and validating XAI explanations, considering the potential for inconsistencies across different tools.
Original post by Louis Peter, Nils Gumpfer, Jana Fischer, Christin Seifert, Jennifer Hannig
"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,…"
View on XOriginally posted by Louis Peter, Nils Gumpfer, Jana Fischer, Christin Seifert, Jennifer Hannig on X · view source
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