INSIGHTS: Demonstration-Based Summaries of Time Series Predictors
The paper introduces INSIGHTS, a model-agnostic approach for providing global explanations of time series models. It emphasizes simplicity, efficiency, and transparency, allowing stakeholders to easily understand model behavior. The evaluation shows that INSIGHTS effectively generates informative summaries that enhance users' comprehension of time series data.
- ▪INSIGHTS focuses on global explanations rather than local, instance-level attributions.
- ▪The approach generates sample summaries that provide a comprehensive overview of model behavior.
- ▪User studies indicate that INSIGHTS-based summaries improve understanding of the model's overall behavior.
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Computer Science > Machine Learning arXiv:2605.18849 (cs) [Submitted on 13 May 2026] Title:INSIGHTS: Demonstration-Based Summaries of Time Series Predictors Authors:Bar Eini Porat, Rom Gutman, Uri Shalit, Ofra Amir View a PDF of the paper titled INSIGHTS: Demonstration-Based Summaries of Time Series Predictors, by Bar Eini Porat and 2 other authors View PDF HTML (experimental) Abstract:Explainability methods have progressed rapidly, but global explanations for time-series models remain underdeveloped, with most approaches focusing on local, instance-level attributions. We introduce INSIGHTS, a model-agnostic, user-centric approach for providing global explanations of time series models.
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