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.

使用多尺度超图转换器（MSHyper）框架，提出了一种更全面的模式交互建模方法，通过引入多尺度超图和超边图来建模高阶模式交互，同时引入三阶段消息传递机制来聚合模式信息和学习不同尺度时间模式之间的交互强度。实验证明，MSHyper 在五个真实世界数据集上取得了最先进的性能，相对于最佳基线方法，在均方误差和平均绝对误差分别平均减少了 8.73% 和 7.15% 的预测误差。