Estimates the moving average (MA) model of a univariate time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.


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Inputs/Outputs

  • c1ddbl.png Xt —

    Xt specifies the univariate time series.

  • cu16.png method —

    method specifies the method to use in estimating the moving average model.

  • ci32.png MA order —

    MA order specifies the order of the moving average model. The value of MA order must be greater than 0. The default is 4.

  • cerrcodeclst.png error in (no error) —

    error in describes error conditions that occur before this node runs. This input provides standard error in functionality.

  • i1ddbl.png MA coefficients —

    MA coefficients returns the estimated coefficients of the moving average model.

  • i1ddbl.png noise —

    noise returns the estimated white noise series of the moving average model.

  • ierrcodeclst.png error out —

    error out contains error information. This output provides standard error out functionality.

    • TSA MA Modeling Details

    This VI estimates the MA model according to the following equation:

    Xt = et + b1et-1 + ,…, + bNet-N

    where N is the MA order, Xt is a univariate time series, and et is a Gaussian white noise series. MA coefficients is a 1D array of [1, b1, b2,…, bN], where each coefficient bi is a real number.

    The minimum length requirement for the input time series differs for each method you use:

    • Yule-Walker method: minimum length >= MA order
    • High-Order AR method: minimum length >= 5 x MA order
    • Polynomial method: minimum length >= 5 x MA order