🤖 AI Summary
This work addresses the challenge of modeling inter-variable dependencies in multivariate time series forecasting, where dense cross-variable interactions often introduce spurious correlations and lead to oversmoothed representations. To overcome this, the authors propose MS-FLOW, a novel framework that, for the first time, formulates cross-variable interaction as a bandwidth-constrained information flow. By employing a sparse routing mechanism under a strict communication budget, MS-FLOW transmits only the most critical dependency signals, thereby realizing an “effective rather than excessive” interaction paradigm. Integrating selective sparse routing with capacity-limited information flow, the method constructs a lightweight yet efficient dependency learning architecture. Evaluated on twelve real-world benchmarks, MS-FLOW achieves state-of-the-art forecasting accuracy while uncovering fewer but more reliable inter-variable dependencies.
📝 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".