Recursive Forecast Updating

Recursive Forecast Updating is a forecasting methodology that iteratively refines point or probabilistic predictions by recursively incorporating ex post observations to adjust the underlying model or parameters, thereby distinguishing itself from non-recursive methods through its systematic feedback of realized outcomes into subsequent forecast horizons.

7

Publications

344

Citations

19

Authors

14

Institutions

Publications per year

2017–2026

0

Authors

19

  1. JW
    Joseph W. Foster

    Texas A&M University, United States

    1 publications · 30 citations

    1

    h-index

  2. DW
    Derek W. Bunn

    London Business School, United Kingdom

    1 publications · 14 citations

    1

    h-index

  3. TS
    Tep Sastri

    Texas A&M University, United States

    1 publications · 30 citations

    1

    h-index

  4. XS
    Xiaosheng Si

    Xi'an High Tech University, China

    1 publications · 16 citations

    1

    h-index

  5. TX
    Tiaojun Xiao

    Nanjing University, China

    1 publications · 62 citations

    1

    h-index

1–5 of 19

Venues

  1. Automatica

    1 publications · 62 citations

    1

    h-index

  2. 1

    h-index

  3. Applied Soft Computing

    1 publications · 50 citations

    1

    h-index

  4. Omega

    1 publications · 14 citations

    1

    h-index

  5. SSRN Electronic Journal

    Repository · 1 publications · 27 citations

    1

    h-index

1–5