Adaptive Exponential Smoothing

Adaptive Exponential Smoothing is a time series forecasting method that dynamically adjusts its smoothing parameters in response to changes in the underlying data pattern, distinguishing itself from standard exponential smoothing techniques by incorporating mechanisms for real-time parameter adaptation based on forecast errors or variance estimates.

74

Publications

7.4K

Citations

168

Authors

95

Institutions

Publications per year

2017–2026

11

Authors

168

  1. JT
    John T. Mentzer

    University of Tennessee at Knoxville, United States

    3 publications · 66 citations

    3

    h-index

  2. HL
    Hong Liang Yu

    Dalian Ocean University, China

    2 publications · 48 citations

    2

    h-index

  3. QK
    Quang Khai Trinh

    Southwest Jiaotong University, China

    2 publications · 39 citations

    2

    h-index

  4. RD
    Ralph D. Snyder

    Monash University, Australia

    2 publications · 1.1K citations

    2

    h-index

  5. SD
    Stephen D. Roberts

    North Carolina State University, United States

    2 publications · 62 citations

    2

    h-index

1–5 of 168

Venues

  1. International Journal of Forecasting

    6 publications · 2.1K citations

    6

    h-index

  2. Journal of the Operational Research Society

    OR · 4 publications · 425 citations

    4

    h-index

  3. Journal of Forecasting

    3 publications · 1.6K citations

    3

    h-index

  4. International Journal of Production Research

    3 publications · 53 citations

    3

    h-index

  5. Operations Research

    3 publications · 159 citations

    3

    h-index

1–5