MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting

Demystifying the 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 feature extraction (MFE) module is introduced to map the input sequence into multi-scale embeddings. Then, a multi-scale hypergraph is designed to provide foundations for modeling high-order pattern interactions and a hyperedge graph is built to enhance hypergraph modeling. In addition, a tri-stage message passing (TMP) mechanism is introduced to aggregate pattern information and learn the interaction strength between temporal patterns of different scales. Extensive experimental results on 8 real-world datasets demonstrate that MSHyper achieves the state-of-the-art (SOTA) performance across various settings.