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

• URL: http://arxiv.org/abs/2401.09261v1
• Date: Wed, 17 Jan 2024 15:12:11 GMT
• Title: MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
• Authors: Zongjiang Shang, Ling Chen
• Abstract summary: We propose a Multi-Scale Hypergraph Transformer (MSHyper) framework to promote more comprehensive pattern interaction modeling. 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.
• Score: 7.178309082582536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 performance, reducing prediction errors by an average of 8.73% and 7.15% over the best baseline in MSE and MAE, respectively.

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