Moving Average Interpolation

Moving Average Interpolation is a data-driven interpolation method that estimates missing values by computing a weighted average of neighboring observed data points, where the weights are determined by a sliding window that systematically moves across the sequence to preserve local trends and reduce noise.

19

Publications

769

Citations

51

Authors

25

Institutions

Publications per year

2017–2026

4

Authors

51

  1. CM
    C. M. Gold

    University of Alberta, Canada

    2 publications · 119 citations

    2

    h-index

  2. TD
    T. D. Charters

    University of Alberta, Canada

    2 publications · 119 citations

    2

    h-index

  3. JR
    J. Ramsden

    University of Alberta, Canada

    2 publications · 119 citations

    2

    h-index

  4. CJ
    Christiaan J. J. Paredis

    Georgia Institute of Technology, United States

    1 publications · 24 citations

    1

    h-index

  5. AA
    Ashray Aggarwal

    Delhi Technological University, India

    1 publications · 55 citations

    1

    h-index

1–5 of 51

Venues

  1. Biometrics

    2 publications · 233 citations

    2

    h-index

  2. Environmental Science & Technology

    1 publications · 30 citations

    1

    h-index

  3. Building Simulation

    1 publications · 24 citations

    1

    h-index

  4. Measurement

    1 publications · 22 citations

    1

    h-index

  5. Environmental Toxicology and Chemistry

    1 publications · 40 citations

    1

    h-index

1–5