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

TL;DR

This paper introduces MS-FLOW, a sparse-bottleneck framework for multivariate time series forecasting that improves reliability by modeling only essential inter-variable dependencies under a limited communication budget.

Contribution

MS-FLOW explicitly models inter-variable interactions as capacity-limited information flow using sparse routing, reducing redundant connections and spurious correlations.

Findings

Achieves state-of-the-art accuracy on 12 benchmarks.

Learns more reliable and effective dependencies.

Reduces redundant and noisy inter-variable connections.

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".