TSA MA Modeling 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

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.

Details  

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

TSA MA Modeling (Waveform)

	Xt specifies the univariate time series.
	method specifies the method to use in estimating the moving average model. Refer to the TSA ARMA Modeling VI for information about these methods. 0 Yule-Walker (default) 1 High order AR 2 Polynomial
	MA order specifies the order of the moving average model. The value of MA order must be greater than 0. The default is 4.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	MA coefficients returns the estimated coefficients of the moving average model.
	noise returns the estimated white noise series of the moving average model.
	error out contains error information. This output provides standard error out functionality.

TSA MA Modeling (Array)

	Xt specifies the univariate time series.
	method specifies the method to use in estimating the moving average model. Refer to the TSA ARMA Modeling VI for information about these methods. 0 Yule-Walker (default) 1 High order AR 2 Polynomial
	MA order specifies the order of the moving average model. The value of MA order must be greater than 0. The default is 4.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	MA coefficients returns the estimated coefficients of the moving average model.
	noise returns the estimated white noise series of the moving average model.
	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:

X_t = e_t + b₁e_t–1 + ,…, + b_Ne_t–N

where N is the MA order, X_t is a univariate time series, and e_t is a Gaussian white noise series. MA coefficients is a 1D array of [1, b1, b2, …, b_N], where each coefficient b_i 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 × MA order
• Polynomial method: minimum length ≥ 5 × MA order