Variable-length Time Series Classification

Variable-Length Time Series Classification is a time series classification task characterized by the requirement to categorize temporal sequences that exhibit varying lengths in duration, distinguishing it from standard time series classification by explicitly addressing the heterogeneity in sequence length.

20

Publications

990

Citations

67

Authors

32

Institutions

Publications per year

2017–2026

11

Authors

67

  1. CC
    C. Chandra Sekhar

    Indian Institute of Technology Madras, India

    2 publications · 34 citations

    2

    h-index

  2. EK
    Eamonn Keogh

    University of California, Riverside, United States

    2 publications · 115 citations

    2

    h-index

  3. SC
    S. Chandrakala

    SASTRA University, India

    1 publications · 14 citations

    1

    h-index

  4. HL
    Henry Liu

    University of Michigan, United States

    1 publications · 31 citations

    1

    h-index

  5. RL
    Ruiyu Liang

    Southeast University, China

    1 publications · 13 citations

    1

    h-index

1–5 of 67

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 235 citations

    4

    h-index

  2. IEEE Access

    1 publications · 23 citations

    1

    h-index

  3. 1

    h-index

  4. Neurocomputing

    1 publications · 17 citations

    1

    h-index

  5. Speech Communication

    1 publications · 20 citations

    1

    h-index

1–5