Explainable Time Series Classification

Explainable Time Series Classification is a time series classification paradigm that, as a singular academic concept, belongs to the class of interpretable machine learning methods by virtue of providing human-understandable justifications for its classification decisions while maintaining predictive accuracy.

29

Publications

1.4K

Citations

104

Authors

57

Institutions

Publications per year

2017–2026

28

Authors

104

  1. AT
    Alexandre Termier

    Centre National de la Recherche Scientifique, France

    3 publications · 243 citations

    3

    h-index

  2. VM
    Véronique Masson

    Centre National de la Recherche Scientifique, France

    3 publications · 243 citations

    3

    h-index

  3. JR
    Jonathan Rebane

    Stockholm University, Sweden

    2 publications · 58 citations

    2

    h-index

  4. FS
    Francesco Spinnato

    University of Pisa, Italy

    2 publications · 212 citations

    2

    h-index

  5. RG
    Riccardo Guidotti

    University of Pisa, Italy

    2 publications · 212 citations

    2

    h-index

1–5 of 104

Venues

  1. Data Mining and Knowledge Discovery

    3 publications · 191 citations

    3

    h-index

  2. Information Sciences

    2 publications · 72 citations

    2

    h-index

  3. 2

    h-index

  4. IEEE Access

    2 publications · 256 citations

    2

    h-index

  5. IEEE Computational Intelligence Magazine

    CIM · 1 publications · 13 citations

    1

    h-index

1–5