Moving-average model

Not to be confused with Moving average.

In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series.[1][2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.

Together with the autoregressive (AR) model, the moving-average model is a special case and key component of the more general ARMA and ARIMA models of time series,[3] which have a more complicated stochastic structure. Contrary to the AR model, the finite MA model is always stationary.

The moving-average model should not be confused with the moving average, a distinct concept despite some similarities.[1]

  1. ^ a b Shumway, Robert H.; Stoffer, David S. (19 April 2017). Time series analysis and its applications : with R examples. Springer. ISBN 978-3-319-52451-1. OCLC 966563984.
  2. ^ "2.1 Moving Average Models (MA models) | STAT 510". PennState: Statistics Online Courses. Retrieved 2023-02-27.
  3. ^ Shumway, Robert H.; Stoffer, David S. (2019-05-17), "ARIMA Models", Time Series: A Data Analysis Approach Using R, Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, pp. 99–128, doi:10.1201/9780429273285-5, ISBN 978-0-429-27328-5, retrieved 2023-02-27{{citation}}: CS1 maint: location (link)