HGTS-Former: Hierarchical HyperGraph Transformer for Multivariate Time Series Analysis
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 44 of its 73 references that are in the map. First posted on arXiv 4 Aug 2025.
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44 of 73 references.
References · 44
- 2017Attention Is All You Needcited by 60,106
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,628
- 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,016
- 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,665
- 2022Are Transformers Effective for Time Series Forecasting?cited by 1,413
- 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,206
- 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,043
- 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 874
- 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 756
- 2024Chronos: Learning the Language of Time Seriescited by 737
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- 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 693
- 2023A decoder-only foundation model for time-series forecastingcited by 674
- 2024Unified Training of Universal Time Series Forecasting Transformerscited by 602
- 2018Hypergraph Neural Networkscited by 485
- 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 408
- 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 341
- 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 286
- 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 246
- 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 205
- 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
- 2021VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flowscited by 128
- 2022GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoningcited by 128
- 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 115
- 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 105
- 2024Timer-XL: Long-Context Transformers for Unified Time Series Forecastingcited by 84
- 2024SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusioncited by 75
- 2023Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baselinecited by 73
- 2025TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecastingcited by 63
- 2024Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modelingcited by 44
- 2024From Similarity to Superiority: Channel Clustering for Time Series Forecastingcited by 37
- 2024Scaling Law for Time Series Forecastingcited by 33
- 2025Temporal Query Network for Efficient Multivariate Time Series Forecastingcited by 30
- 2023Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusioncited by 24
- 2024Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecastingcited by 21
- 2025CrossLinear: Plug-and-Play Cross-Correlation Embedding for Time Series Forecasting with Exogenous Variablescited by 21
- 2023SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognitioncited by 20
- 2025Hypergraph Vision Transformers: Images are More than Nodes, More than Edgescited by 16
- 2020Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseriescited by 13
- 2024MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecastingcited by 12
- 2024TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysiscited by 10
- 2025TimesBERT: A BERT-Style Foundation Model for Time Series Understandingcited by 10
- 2025Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generationcited by 1
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