Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

• URL: http://arxiv.org/abs/2607.28124v1
• Date: Thu, 30 Jul 2026 12:33:19 GMT
• Title: Information Bottleneck Learning for Faithful Time Series Forecasting Explanations
• Abstract summary: We propose IB-Forecast, an inherently interpretable multivariate time-series forecasting framework.<n>It decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens.<n>With a budget-constrained information bottleneck, end-to-end optimization enables users to directly control explanation sparsity.
• Score: 26.577036929886486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predictions has become as crucial as the predictions themselves. Although existing interpretable-by-design forecasters reveal their internal structures, they offer no guarantee that these structures faithfully reflect the underlying evidence driving the predictions. In contrast, while faithfulness-oriented methods explicitly verify model behavior, they are almost exclusively designed for post-hoc classification tasks. To bridge this gap, we propose IB-Forecast, an inherently interpretable multivariate time-series forecasting framework. It decomposes forecasting into a learned periodic component and a residual component computed with explainable masks over input tokens. With a budget-constrained information bottleneck, end-to-end optimization enables users to directly control explanation sparsity. With a rigorous faithfulness evaluation protocol, extensive experiments demonstrate that IB-Forecast matches the forecasting error of leading black-box models while providing faithful explanations at no additional inference cost. Furthermore, under a matched sparsity budget, these native explanations consistently surpass gradient-based, occlusion-based, and optimization-based baselines across all evaluated datasets. Ultimately, whereas the native explanations of existing interpretable forecasters exhibit poor faithfulness, IB-Forecast guarantees high explanation fidelity, requiring only 14-20% of the observations to deliver low-error predictions.

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