Multivariate (Vector) adaptive AR estimation base on a multidimensional Kalman filer algorithm. A standard VAR model (A0=I) is implemented. The state vector is defined as X=(A1|A2...|Ap) and x=vec(X') [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman) The standard MVAR model is defined as: y(n)-A1(n)*y(n-1)-...-Ap(n)*y(n-p)=e(n) The dimension of y(n) equals s Input Parameters: y Observed data or signal p prescribed maximum model order (default 1) UC update coefficient (default 0.001) mode update method of the process noise covariance matrix 0...4 ^ correspond to S0...S4 (default 0) Output Parameters e prediction error of dimension s x state vector of dimension s*s*p Q2 measurement noise covariance matrix of dimension s x s
Package: tsa