# TSA ARMA Prediction VI

LabVIEW 2014 Advanced Signal Processing Toolkit Help

Edition Date: June 2014

Part Number: 372656C-01

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Owning Palette: Modeling and Prediction VIs

Requires: Advanced Signal Processing Toolkit

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.

Example

Use the pull-down menu to select an instance of this VI.

 Select an instance TSA ARMA Prediction (Waveform)TSA ARMA Prediction (Array)TSA Vector ARMA Prediction (Waveform)TSA Vector ARMA Prediction (Array)

## TSA ARMA Prediction (Waveform)

 number of points specifies the length of the predicted time series. The default is 1. Xt specifies the univariate time series. AR coefficients specifies the AR coefficients of the autoregressive-moving average model. You can obtain the AR coefficients using the TSA ARMA Modeling VI. MA coefficients specifies the MA coefficients of the autoregressive-moving average model. You can obtain the MA coefficients using the TSA ARMA Modeling VI. error in describes error conditions that occur before this node runs. This input provides standard error in functionality. noise variance specifies the variance of the white noise series of the autoregressive-moving average model. predicted series returns the predicted univariate time series. standard deviation returns the standard deviation of each predicted value. error out contains error information. This output provides standard error out functionality.

## TSA ARMA Prediction (Array)

 number of points specifies the length of the predicted time series. The default is 1. Xt specifies the univariate time series. AR coefficients specifies the AR coefficients of the autoregressive-moving average model. You can obtain the AR coefficients using the TSA ARMA Modeling VI. MA coefficients specifies the MA coefficients of the autoregressive-moving average model. You can obtain the MA coefficients using the TSA ARMA Modeling VI. error in describes error conditions that occur before this node runs. This input provides standard error in functionality. noise variance specifies the variance of the white noise series of the autoregressive-moving average model. predicted series returns the predicted univariate time series. standard deviation returns the standard deviation of each predicted value. error out contains error information. This output provides standard error out functionality.

## TSA Vector ARMA Prediction (Waveform)

 number of points specifies the length of the predicted time series. The default is 1. Xt specifies the multivariate (vector) time series. AR coefficients specifies the estimated AR coefficients of the vector autoregressive-moving average model. MA coefficients specifies the estimated MA coefficients of the vector autoregressive-moving average model. error in describes error conditions that occur before this node runs. This input provides standard error in functionality. noise covariance specifies the covariance matrix of the estimated multivariate white noise series of the vector autoregressive-moving average model. predicted series returns the predicted multivariate time series. standard deviation returns the standard deviation of the predicted multivariate values. error out contains error information. This output provides standard error out functionality.

## TSA Vector ARMA Prediction (Array)

 number of points specifies the length of the predicted time series. The default is 1. Xt specifies the multivariate (vector) time series. Each column of the 2D array represents a vector at certain time. AR coefficients specifies the estimated AR coefficients of the vector autoregressive-moving average model. MA coefficients specifies the estimated MA coefficients of the vector autoregressive-moving average model. error in describes error conditions that occur before this node runs. This input provides standard error in functionality. noise covariance specifies the covariance matrix of the estimated multivariate white noise series of the vector autoregressive-moving average model. predicted series returns the predicted multivariate time series. Each column of the 2D array represents a vector at certain time. standard deviation returns the standard deviation of the predicted multivariate values. Each column of the 2D array represents a vector at certain time. error out contains error information. This output provides standard error out functionality.

## Example

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.