Demystifying interactions between temporal patterns of different scales is fundamental to precise long-range time series forecasting. However, previous works lack the ability to model high-order interactions. To promote more comprehensive pattern interaction modeling for long-range time series forecasting, we propose a Multi-Scale Hypergraph Transformer (MSHyper) framework. Specifically, a multi-scale hypergraph is introduced to provide foundations for modeling high-order pattern interactions. Then by treating hyperedges as nodes, we also build a hyperedge graph to enhance hypergraph modeling. In addition, a tri-stage message passing mechanism is introduced to aggregate pattern information and learn the interaction strength between temporal patterns of different scales. Extensive experiments on five real-world datasets demonstrate that MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 8.73% and 7.15% over the best baseline in MSE and MAE, respectively.

翻译：揭示不同尺度时序模式之间的相互作用是精准长程时间序列预测的基础。然而，现有研究缺乏对高阶交互的建模能力。为促进长程时间序列预测中更全面的模式交互建模，我们提出多尺度超图Transformer（MSHyper）框架。具体而言，通过引入多尺度超图为高阶模式交互建模提供基础，进而将超边视为节点构建超边图以增强超图建模。此外，设计三阶段消息传递机制用于聚合模式信息并学习不同尺度时序模式间的交互强度。在五个真实数据集上的大量实验表明，MSHyper实现了最先进性能，在最佳基线基础上平均降低均方误差8.73%、平均绝对误差7.15%。