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.


icon

Inputs/Outputs

  • ci32.png number of points —

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

  • c2ddbl.png Xt —

    Xt specifies the multivariate (vector) time series. Each column of the 2D array represents a vector at certain time.

  • c1dcclst.png AR coefficients —

    AR coefficients specifies the estimated AR coefficients of the vector autoregressive-moving average model.

  • c2ddbl.png —

  • c1dcclst.png MA coefficients —

    MA coefficients specifies the estimated MA coefficients of the vector autoregressive-moving average model.

  • c2ddbl.png —

  • cerrcodeclst.png error in (no error) —

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

  • c2ddbl.png noise covariance —

    noise covariance specifies the covariance matrix of the estimated multivariate white noise series of the vector autoregressive-moving average model.

  • i2ddbl.png predicted series —

    predicted series returns the predicted multivariate time series. Each column of the 2D array represents a vector at certain time.

  • i2ddbl.png standard deviation —

    standard deviation returns the standard deviation of the predicted multivariate values. Each column of the 2D array represents a vector at certain time.

  • 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.