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arXiv · 2401.09261

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

Zongjiang Shang·Ling Chen·Binqing Wu·Dongliang Cui
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Abstract

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 (SOTA) performance across various settings.

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Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui. 2024-01-17. MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting. https://arxiv.org/abs/2401.09261

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