What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies

• URL: http://arxiv.org/abs/2605.08289v1
• Date: Fri, 08 May 2026 08:16:41 GMT
• Title: What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
• Abstract summary: Multivariable time series forecasting is critical in many real-world systems.<n> dependencies in real data are often state-dependent and noisy.<n>We propose MS-FLOW, a sparse-bottleneck framework that explicitly models inter-variable interaction as capacity-limited information flow.
• Score: 6.1337977581640075
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multivariate time series forecasting is critical in many real-world systems, and thus modeling cross-channel dependencies is essential. Although existing methods improve overall accuracy by enhancing representations and cross-channel interactions, it remains challenging to reliably capture inter-variable dependencies under specific conditions. We observe that dependencies in real data are often state-dependent and noisy; in such cases, dense interactions can amplify spurious correlations and lead to representation over-smoothing, which may yield unreliable predictions in certain scenarios. Motivated by this, we propose MS-FLOW, a sparse-bottleneck framework that explicitly models inter-variable interaction as capacity-limited information flow. Specifically, MS-FLOW replaces fully connected communication with selective sparse routing, retaining only a few critical dependency paths and injecting cross-variable signals under a strict communication budget, thereby suppressing redundant connections and spurious-correlation propagation. Extensive experiments demonstrate that MS-FLOW learns more reliable multivariate correlations, achieving state-of-the-art forecasting accuracy on 12 real-world benchmarks while producing fewer yet more reliable dependencies, shifting multivariate forecasting from "more interaction" to "more effective interaction".

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