INSIGHTS: Demonstration-Based Summaries of Time Series Predictors

2026-05-13

AI Summary

Existing time series models lack effective methods for global interpretability, often limited to local explanations. This work introduces example-based global explanations to time series forecasting for the first time, proposing a model-agnostic, user-centered approach that automatically constructs representative subsets of time series by balancing sample importance and diversity through a domain-specific utility function, thereby generating concise and transparent summaries of global model behavior. Experiments demonstrate that the method efficiently produces high-quality summaries that are readily amenable to human evaluation. User studies further reveal that domain experts significantly prefer the model understanding provided by this approach, leading to a marked improvement in their comprehension of overall model behavior.

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. Our approach prioritizes simplicity, efficiency, and transparency in its design, ensuring that stakeholders can readily adopt its outputs. While current methods focus on local explanations, INSIGHTS generates sample summaries that offer a comprehensive overview of model behavior. It balances the importance and diversity of time series samples to create informative subsets using utility functions that capture domain-specific aspects of time series behavior, such as exceeding domain norms. We evaluate INSIGHTS through experiments, interviews, and a user study. Our results indicate INSIGHTS effectively constructs comprehensive, diverse time series subsets, producing summaries manageable for individual evaluation. It is preferred by domain experts for its ability to provide a stable understanding of model behavior and the quality of the samples identified. Moreover, user study participants presented with INSIGHTS-based summaries exhibit an enhanced understanding of the model's overall behavior.

Problem

• time series
• explainability
• global explanations
• model interpretability
• summary generation

Innovation

• global explanation
• time series
• model interpretability
• sample summarization
• user-centric design