Point Process Prediction

Point Process Prediction is a methodological framework within stochastic process analysis that enables the forecasting of future event times and locations by modeling the conditional intensity function of a point process based on its historical realization.

39

Publications

1.4K

Citations

102

Authors

55

Institutions

Publications per year

2017–2026

18

Authors

102

  1. HZ
    Hongyuan Zha

    Georgia Institute of Technology, United States

    4 publications · 83 citations

    4

    h-index

  2. LS
    Le Song

    Georgia Institute of Technology, United States

    3 publications · 58 citations

    3

    h-index

  3. BK
    Baeho Kim

    Korea University, South Korea

    2 publications · 51 citations

    2

    h-index

  4. DE
    Donald E. Brown

    University of Virginia, United States

    2 publications · 40 citations

    2

    h-index

  5. KG
    Kay Giesecke

    Stanford University, United States

    2 publications · 51 citations

    2

    h-index

1–5 of 102

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 118 citations

    4

    h-index

  2. Journal of Applied Probability

    JAP · 2 publications · 60 citations

    2

    h-index

  3. IEEE Transactions on Multimedia

    1 publications · 40 citations

    1

    h-index

  4. 1

    h-index

  5. Quantitative Finance

    1 publications · 196 citations

    1

    h-index

1–5