Related papers: Towards A Unified Information Bottleneck Framework for Time Series Explanations

Towards A Unified Information Bottleneck Framework for Time Series Explanations

URL: http://arxiv.org/abs/2608.25897v1
Date: Wed, 26 Aug 2026 15:14:52 GMT
Title: Towards A Unified Information Bottleneck Framework for Time Series Explanations
Abstract summary: We show that existing explainers are vulnerable to trivial solutions and distributional shifts.<n>We propose a unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning.<n>Our formulation explicitly prevents trivial explanations and out-of-distribution counterfactuals.
Score: 27.646760316510356
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods generally fall into two categories: attribution-based explanations, which identify the temporal regions most responsible for a prediction, and counterfactual explanations, which reveal how an input should be modified to alter the model's decision.} {Despite valuable insights, these two fields are largely studied independently. This disconnect leaves attribution methods lacking causal validation, while counterfactual methods suffer from severe instability, producing adversarial-like noise instead of meaningful explanations.} In this work, we revisit time-series explainability from an information-theoretic perspective and show that existing explainers are vulnerable to trivial solutions and distributional shifts. To address these limitations, we propose a unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework. Building upon the Information Bottleneck principle, our formulation explicitly prevents trivial explanations and out-of-distribution counterfactuals. {Based on this objective function, we introduce {\modelname}, a novel explanation framework that learns a parametric transformation network to construct explanation-embedded instances, where preserved information yields attribution explanations and controlled information removal produces stable counterfactual explanations.} We evaluate {\modelname} on synthetic and real-world benchmarks against state-of-the-art baselines. Extensive quantitative and qualitative results show that {\modelname} consistently outperforms competing methods, yielding faithful attributions and stable counterfactual explanations.

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