提出ST-Hyper模型，精准捕捉多尺度时空依赖关系。
ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
- 构建时空金字塔模块提取多尺度特征
- 通过自适应超图学习高阶依赖，长短期预测误差分别降低6.8%和3.8%
- 适合需要精细建模复杂时序依赖的科研与工业场景
在多变量时间序列（MTS）预测中，现有深度学习方法虽能建模空间或时间尺度上的依赖关系，但难以有效捕捉同时包含空间与时间范围的多尺度时空依赖（ST-scales）。本文提出ST-Hyper，通过自适应超图建模实现跨多尺度的高阶依赖建模。具体地，设计时空金字塔建模（STPM）模块以提取多尺度特征，并引入自适应超图建模（AHM）模块，学习稀疏超图以捕获鲁棒的高阶特征依赖。此外，通过三阶段超图传播机制，全面捕捉多尺度时空动态。在六个真实世界MTS数据集上的实验表明，该方法达到当前最优性能，相较于最佳基线，长期与短期预测的平均MAE分别降低6.8%和3.8%。
原文摘要 · Abstract (English)
In multivariate time series (MTS) forecasting, many deep learning based methods have been proposed for modeling dependencies at multiple spatial (inter-variate) or temporal (intra-variate) scales. However, existing methods may fail to model dependencies across multiple spatial-temporal scales (ST-scales, i.e., scales that jointly consider spatial and temporal scopes). In this work, we propose ST-Hyper to model the high-order dependencies across multiple ST-scales through adaptive hypergraph modeling. Specifically, we introduce a Spatial-Temporal Pyramid Modeling (STPM) module to extract features at multiple ST-scales. Furthermore, we introduce an Adaptive Hypergraph Modeling (AHM) module that learns a sparse hypergraph to capture robust high-order dependencies among features. In addition, we interact with these features through tri-phase hypergraph propagation, which can comprehensively capture multi-scale spatial-temporal dynamics. Experimental results on six real-world MTS datasets demonstrate that ST-Hyper achieves the state-of-the-art performance, outperforming the best baselines with an average MAE reduction of 3.8\% and 6.8\% for long-term and short-term forecasting, respectively.
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