Self-supervised Time Series Classification

Self-Supervised Time Series Classification is a methodological framework within time series classification that leverages self-supervised learning to extract discriminative representations from unlabeled time series data by designing pretext tasks that capture temporal dynamics, thereby enabling effective classification without relying on labeled training instances.

51

Publications

2.9K

Citations

223

Authors

94

Institutions

Publications per year

2017–2026

51

Authors

223

  1. HF
    Haoyi Fan

    Harbin University of Science and Technology, China

    4 publications · 150 citations

    4

    h-index

  2. FZ
    Fengbin Zhang

    Harbin University of Science and Technology, China

    3 publications · 99 citations

    3

    h-index

  3. SP
    Shirui Pan

    Monash University, Australia

    3 publications · 334 citations

    3

    h-index

  4. RC
    Rongyao Cai

    Zhejiang University, China

    2 publications · 227 citations

    2

    h-index

  5. RW
    Ruidong Wang

    Stanford University, United States

    2 publications · 102 citations

    2

    h-index

1–5 of 223

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 451 citations

    9

    h-index

  2. Knowledge-Based Systems

    3 publications · 238 citations

    3

    h-index

  3. Neurocomputing

    3 publications · 112 citations

    3

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 72 citations

    2

    h-index

  5. 2

    h-index

1–5