Implet: A Post-hoc Subsequence Explainer for Time Series Models
This addresses the need for better interpretability in time series models for applications like debugging and trust-building, though it appears incremental as it builds on existing post-hoc explanation methods.
The paper tackles the problem of explainability in time series models by introducing Implet, a post-hoc explainer that identifies critical temporal subsequences to enhance interpretability, showing effectiveness on standard benchmarks.
Explainability in time series models is crucial for fostering trust, facilitating debugging, and ensuring interpretability in real-world applications. In this work, we introduce Implet, a novel post-hoc explainer that generates accurate and concise subsequence-level explanations for time series models. Our approach identifies critical temporal segments that significantly contribute to the model's predictions, providing enhanced interpretability beyond traditional feature-attribution methods. Based on it, we propose a cohort-based (group-level) explanation framework designed to further improve the conciseness and interpretability of our explanations. We evaluate Implet on several standard time-series classification benchmarks, demonstrating its effectiveness in improving interpretability. The code is available at https://github.com/LbzSteven/implet