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Causal and Local Correlations Based Network for Multivariate Time Series Classification

arXiv 2411.18008 ↗First posted 27 Nov 2024cs.LG cs.AI stat.MEcited by 0

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Abstract (arXiv)

Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 61 references that are in the map. First posted on arXiv 27 Nov 2024.

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References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  7. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  8. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  9. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  10. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  11. 2018Multivariate LSTM-FCNs for Time Series Classificationcited by 120
  12. 2021Gated Transformer Networks for Multivariate Time Series Classificationcited by 58
12 more
  1. 2017Multivariate Time Series Classification with WEASEL+MUSEcited by 53
  2. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 45
  3. 2018A review on distance based time series classificationcited by 40
  4. 2023Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Datacited by 23
  5. 2023FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classificationcited by 21
  6. 2018Detecting and interpreting myocardial infarction using fully convolutional neural networkscited by 19
  7. 2023TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classificationcited by 16
  8. 2022Towards Similarity-Aware Time-Series Classificationcited by 13
  9. 2020RTFN: A Robust Temporal Feature Network for Time Series Classificationcited by 11
  10. 2020Multivariate Time Series Forecasting with Transfer Entropy Graphcited by 10
  11. 2021SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Seriescited by 9
  12. 2022Enhancing Transformer Efficiency for Multivariate Time Series Classificationcited by 4

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