Forecasting With Missing Data

Forecasting with Missing Data is a methodological framework in epidemic forecasting that integrates statistical or machine learning techniques to generate reliable predictions despite incomplete or partially observed epidemiological data by explicitly modeling the mechanisms and patterns of data absence.

83

Publications

3.7K

Citations

335

Authors

148

Institutions

Publications per year

2017–2026

55

Authors

335

  1. ÉG
    Éric Gaussier

    Laboratoire d'Informatique de Grenoble, France

    2 publications · 110 citations

    2

    h-index

  2. PG
    Parantapa Goswami

    Université Grenoble Alpes, France

    2 publications · 110 citations

    2

    h-index

  3. XT
    Xianfeng Tang

    Pennsylvania State University, United States

    2 publications · 133 citations

    2

    h-index

  4. DO
    Dave Osthus

    Los Alamos National Laboratory, United States

    2 publications · 54 citations

    2

    h-index

  5. HM
    Hamid Mirisaee

    Laboratoire d'Informatique de Grenoble, France

    2 publications · 110 citations

    2

    h-index

1–5 of 335

Venues

  1. 4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 90 citations

    3

    h-index

  3. Pattern Recognition Letters

    2 publications · 105 citations

    2

    h-index

  4. Journal of Forecasting

    2 publications · 34 citations

    2

    h-index

  5. Journal of Computer Applications

    2 publications · 46 citations

    2

    h-index

1–5