Dynamic Time Warping

Dynamic Time Warping is a dynamic-programming-based time series similarity measure that computes the minimal cumulative distance between two sequences by optimally and nonlinearly aligning (warping) their time indices to allow local stretching and compression in the presence of temporal misalignment or differing lengths.

1.2K

Publications

88K

Citations

3.3K

Authors

1.2K

Institutions

Publications per year

2017–2026

444

Authors

3.3K

  1. EK
    Eamonn Keogh

    University of California, Riverside, United States

    37 publications · 10.5K citations

    32

    h-index

  2. LR
    L. R. Rabiner

    AT&T (United States), United States

    13 publications · 1.9K citations

    13

    h-index

  3. AM
    Abdullah Mueen

    University of New Mexico, United States

    11 publications · 2.1K citations

    11

    h-index

  4. CA
    Chotirat Ann Ratanamahatana

    Chulalongkorn University, Thailand

    12 publications · 4.2K citations

    11

    h-index

  5. FP
    François Petitjean

    Monash University, Australia

    11 publications · 2.2K citations

    11

    h-index

1–5 of 3.3K

Venues

  1. arXiv (Cornell University)

    Repository · 29 publications · 2.4K citations

    21

    h-index

  2. IEEE Access

    24 publications · 908 citations

    18

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 17 publications · 3.4K citations

    17

    h-index

  4. Sensors

    22 publications · 1.5K citations

    17

    h-index

  5. 16

    h-index

1–5