This section describes a collection of Kalman filtering and smoothing subroutines for time series analysis; immediately following are three examples that demonstrate how to use Kalman filtering subroutines. The state space model (SSM) is a method of analyzing a wide range of time series models. When the time series is represented by the state space model, the Kalman filter is used for filtering, prediction, and smoothing of the state vector. The state space model consists of the measurement and transition equations.
SAS/IML software supports the following Kalman filtering and smoothing subroutines:
performs covariance filtering and prediction.
performs fixed-interval smoothing.
performs diffuse covariance filtering and prediction.
performs diffuse fixed-interval smoothing.