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

• URL: http://arxiv.org/abs/2401.09261v2
• Date: Mon, 23 Dec 2024 06:08:16 GMT
• Title: MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
• Authors: Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui,
• Abstract summary: We propose a Multi-Scale Hypergraph Transformer (MSHyper) framework to promote more comprehensive pattern interaction modeling. MSHyper achieves state-of-the-art (SOTA) performance across various settings.
• Score: 5.431115840202783
• License:
• 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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