{"ID":2886928,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.02411","arxiv_id":"2508.02411","title":"HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis","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 on multiple representative time series analysis tasks and public datasets fully validated the effectiveness of our proposed HGTS-Former. Moreover, we present EAST-ELM640, a large-scale time series dataset for Edge-Localized Mode (ELM) recognition in nuclear fusion, on which we achieve state-of-the-art performance. The source code will be released on https://github.com/Event-AHU/Time_Series_Analysis","short_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 stron...","url_abs":"https://arxiv.org/abs/2508.02411","url_pdf":"https://arxiv.org/pdf/2508.02411v2","authors":"[\"Hao Si\",\"Xiao Wang\",\"Fan Zhang\",\"Xiaoya Zhou\",\"Dengdi Sun\",\"Wanli Lyu\",\"Qingquan Yang\",\"Jin Tang\"]","published":"2025-08-04T13:33:28Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":611383,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2886928,"paper_url":"https://arxiv.org/abs/2508.02411","paper_title":"HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis","repo_url":"https://github.com/Event-AHU/Time_Series_Analysis","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}