  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.
