Autoregressive Moving Average Models

Autoregressive Moving Average Models is a statistical time series model that combines autoregressive and moving average components to capture temporal dependence by expressing the current value as a linear combination of past values and past error terms.

1.8K

Publications

129.7K

Citations

3.8K

Authors

1.6K

Institutions

Publications per year

2017–2026

428

Variants

Arma ModelAutoregressive Moving Average Model

Authors

3.8K

  1. GC
    George C. Tiao

    University of Wisconsin–Madison, United States

    14 publications · 3K citations

    14

    h-index

  2. JM
    Jerry M. Mendel

    University of Southern California, United States

    14 publications · 3.1K citations

    13

    h-index

  3. FD
    Feng Ding

    Jiangnan University, China

    13 publications · 680 citations

    12

    h-index

  4. RS
    Ruey S. Tsay

    University of Chicago, United States

    12 publications · 1K citations

    12

    h-index

  5. GB
    Georgios B. Giannakis

    University of Minnesota, United States

    12 publications · 1.1K citations

    11

    h-index

1–5 of 3.8K

Venues

  1. Biometrika

    93 publications · 27.8K citations

    46

    h-index

  2. Journal of Time Series Analysis

    102 publications · 6.4K citations

    38

    h-index

  3. Journal of the American Statistical Association

    54 publications · 10.7K citations

    33

    h-index

  4. 25

    h-index

  5. Journal of Business and Economic Statistics

    JBES · 32 publications · 3.1K citations

    23

    h-index

1–5