Spatio-temporal Missing Data Imputation

Spatio-Temporal Missing Data Imputation is a computational method in spatial time series analysis that specifically reconstructs unobserved or corrupted observations by leveraging both spatial dependencies and temporal dynamics inherent in spatio-temporal datasets.

216

Publications

10.4K

Citations

645

Authors

292

Institutions

Publications per year

2017–2026

152

Variants

Spatiotemporal Missing Data Imputation

Authors

645

  1. LS
    Lijun Sun

    McGill University, Canada

    7 publications · 1.1K citations

    7

    h-index

  2. XC
    Xinyu Chen

    McGill University, Canada

    5 publications · 1K citations

    5

    h-index

  3. XC
    Xiaobo Chen

    Jiangsu University, China

    2 publications · 50 citations

    3

    h-index

  4. YW
    Yinhai Wang

    University of Washington, United States

    3 publications · 177 citations

    3

    h-index

  5. WZ
    Wenfeng Zhou

    Zhejiang University of Technology, China

    3 publications · 172 citations

    3

    h-index

1–5 of 645

Venues

  1. 14

    h-index

  2. 9

    h-index

  3. arXiv (Cornell University)

    Repository · 6 publications · 251 citations

    6

    h-index

  4. 5

    h-index

  5. IEEE Internet of Things Journal

    4 publications · 160 citations

    4

    h-index

1–5