如果让预测忘记呢？一种用于跨变量依赖的稀疏瓶颈
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
AI总结：
本文提出MS-FLOW框架，通过稀疏路由建模跨变量依赖，提升多变量预测的可靠性与准确性。
摘要：
多变量时间序列预测在许多现实系统中至关重要，因此建模跨通道依赖是必要的。尽管现有方法通过增强表示和跨通道交互来提高整体准确性，但在特定条件下仍难以可靠捕捉跨变量依赖。我们观察到真实数据中的依赖通常是状态依赖且噪声的；在这种情况下，密集交互会放大伪相关性并导致表示过平滑，可能在某些场景下产生不可靠的预测。受此启发，我们提出MS-FLOW，一种稀疏瓶颈框架，明确将跨变量交互建模为容量受限的信息流。具体而言，MS-FLOW用选择性稀疏路由替代全连接通信，仅保留少数关键依赖路径，并在严格通信预算下注入跨变量信号，从而抑制冗余连接和伪相关性传播。广泛实验表明，MS-FLOW学习了更可靠的多变量相关性，在12个现实基准上实现了最先进的预测准确性，同时产生更少但更可靠的依赖，将多变量预测从'更多交互'转向'更有效的交互'。
英文摘要：
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".
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
