Temporal Imputation

Temporal Imputation is a temporal feature engineering technique that specifically addresses the restoration of missing or irregularly sampled data points in time series by leveraging temporal dependencies and structural patterns unique to chronological sequences.

29

Publications

799

Citations

113

Authors

56

Institutions

Publications per year

2017–2026

21

Authors

113

  1. YL
    Yuanyuan Li

    Shanghai University of Engineering Science, China

    2 publications · 74 citations

    2

    h-index

  2. LE
    Lynne E. Parker

    University of Tennessee at Knoxville, United States

    2 publications · 74 citations

    2

    h-index

  3. AM
    Anastasia Moiseeva

    State Research Center of Virology and Biotechnology VECTOR, Russia

    1 publications · 11 citations

    1

    h-index

  4. SK
    Seyun Kim

    Seoul National University, South Korea

    1 publications · 20 citations

    1

    h-index

  5. SR
    Sara R. Jaeger

    Plant & Food Research, New Zealand

    1 publications · 20 citations

    1

    h-index

1–5 of 113

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 73 citations

    2

    h-index

  2. IEEE Transactions on Biomedical Engineering

    1 publications · 12 citations

    1

    h-index

  3. IEEE Sensors Journal

    1 publications · 32 citations

    1

    h-index

  4. Information Sciences

    1 publications · 21 citations

    1

    h-index

  5. Applied Intelligence

    1 publications · 30 citations

    1

    h-index

1–5