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.


icon

TSA MA Modeling Details

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

Xt = et + b1et-1 + ,…, + bNet-N

where N is the MA order, Xt is a univariate time series, and et is a Gaussian white noise series. MA coefficients is a 1D array of [1, b1, b2,…, bN], where each coefficient bi 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 x MA order
  • Polynomial method: minimum length >= 5 x MA order