Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification

Author:

Xi WenjieORCID,Jain Arnav,Zhang LiORCID,Lin JessicaORCID

Publisher

Springer Nature Singapore

Reference19 articles.

1. Alfke, D., Gondos, M., Peroche, L., Stoll, M.: An empirical study of graph-based approaches for semi-supervised time series classification. arXiv preprint arXiv:2104.08153 (2021)

2. Bagnall, A., et al.: The UEA multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075 (2018)

3. Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2017)

4. Cheng, Z., et al.: Time2graph+: bridging time series and graph representation learning via multiple attentions. IEEE Trans. Knowl. Data Eng. 35(2), 2078–2090 (2021)

5. Dau, H.A., et al.: The UCR time series archive. IEEE/CAA J. Automatica Sinica 6(6), 1293–1305 (2019)

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