Predicts the values of a univariate or multivariate (vector) time series based on the autoregressive-moving average (ARMA) model. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.


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Inputs/Outputs

  • ci32.png number of points —

    number of points specifies the length of the predicted time series. The default is 1.

  • c1ddbl.png Xt —

    Xt specifies the univariate time series.

  • c1ddbl.png AR coefficients —

    AR coefficients specifies the AR coefficients of the autoregressive-moving average model. You can obtain the AR coefficients using the TSA ARMA Modeling VI.

  • c1ddbl.png MA coefficients —

    MA coefficients specifies the MA coefficients of the autoregressive-moving average model. You can obtain the MA coefficients using the TSA ARMA Modeling VI.

  • cerrcodeclst.png error in (no error) —

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

  • cdbl.png noise variance —

    noise variance specifies the variance of the white noise series of the autoregressive-moving average model.

  • i1ddbl.png predicted series —

    predicted series returns the predicted univariate time series.

  • i1ddbl.png standard deviation —

    standard deviation returns the standard deviation of each predicted value.

  • ierrcodeclst.png error out —

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

    • Examples

    Refer to the ARMA Prediction VI in the labview\examples\Time Series Analysis\TSAGettingStarted directory for an example of using the TSA ARMA Prediction VI.