Time Series Classification Under Missing Data

Time Series Classification Under Missing Data is a methodological framework within time series classification that specifically addresses the challenge of assigning class labels to temporal sequences when portions of the data are unobserved or incomplete.

22

Publications

3.5K

Citations

68

Authors

45

Institutions

Publications per year

2017–2026

15

Authors

68

  1. ZC
    Zhengping Che

    University of Southern California, United States

    2 publications · 2.4K citations

    2

    h-index

  2. YL
    Yan Liu

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 2.4K citations

    2

    h-index

  3. DS
    David Sontag

    Massachusetts Institute of Technology, United States

    2 publications · 2.4K citations

    2

    h-index

  4. SP
    Sanjay Purushotham

    University of Southern California, United States

    2 publications · 2.4K citations

    2

    h-index

  5. KC
    Kyunghyun Cho

    New York University, United States

    2 publications · 2.4K citations

    2

    h-index

1–5 of 68

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 756 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 15 citations

    1

    h-index

  3. 1

    h-index

  4. IEEE Access

    1 publications · 23 citations

    1

    h-index

  5. 1

    h-index

1–5