HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis

• URL: http://arxiv.org/abs/2508.02411v1
• Date: Mon, 04 Aug 2025 13:33:28 GMT
• Title: HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis
• Authors: Xiao Wang, Hao Si, Fan Zhang, Xiaoya Zhou, Dengdi Sun, Wanli Lyu, Qingquan Yang, Jin Tang,
• Abstract summary: This paper proposes a novel hypergraph-based time series transformer backbone network, termed HGTS-Former.<n>We first normalize and embed each patch into tokens. Then, we adopt the multi-head self-attention to enhance the temporal representation of each patch.<n>The hierarchical hypergraphs are constructed to aggregate the temporal patterns within each channel and fine-grained relations between different variables.
• Score: 14.388097471205102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time series analysis has long been one of the key research topics in the field of artificial intelligence. However, analyzing complex time series data remains a challenging and unresolved problem due to its high dimensionality, dynamic nature, and complex interactions among variables. Inspired by the strong structural modeling capability of hypergraphs, this paper proposes a novel hypergraph-based time series transformer backbone network, termed HGTS-Former, to address the multivariate coupling in time series data. Specifically, given the multivariate time series signal, we first normalize and embed each patch into tokens. Then, we adopt the multi-head self-attention to enhance the temporal representation of each patch. The hierarchical hypergraphs are constructed to aggregate the temporal patterns within each channel and fine-grained relations between different variables. After that, we convert the hyperedge into node features through the EdgeToNode module and adopt the feed-forward network to further enhance the output features. Extensive experiments conducted on two multivariate time series tasks and eight datasets fully validated the effectiveness of our proposed HGTS-Former. The source code will be released on https://github.com/Event-AHU/Time_Series_Analysis.

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