TSA AR Modeling VI

TSA AR Modeling Details

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

X_t + a₁X_t-1 + ,…, + a_nX_t-n = e_t

where n is the AR order. X_t is a univariate or multivariate (vector) time series. e_t is a Gaussian white noise series with a mean of zero.

For univariate time series, AR coefficients is a 1D array of [1, a₁, a₂,…, a_n], where each coefficient a_i is a real number.

For multivariate time series, AR coefficients is a 1D array of [I, a₁, a₂,…, a_n], where each coefficient a_i is a cluster of 2D arrays.

The minimum length requirement for the input time series needs to be at least two times the AR order.

Examples

Refer to the following VIs for examples of using the TSA AR Modeling VI:

• Power Line Monitor VI: labview\examples\Time Series Analysis\TSAApplications
• Fault Detection with Pattern Recognition VI: labview\examples\Time Series Analysis\TSAApplications
• AR Model Estimation VI: labview\examples\Time Series Analysis\TSAGettingStarted