This section describes a collection of Kalman filtering and smoothing functions for time series analysis; immediately following are two examples using Kalman filtering functions. The state space model is a method for analyzing a wide range of time series models. When the time series is represented by the state space model (SSM), the Kalman filter is used for filtering, prediction, and smoothing of the state vector. The state space model is composed of the measurement and transition equations.

The following Kalman filtering and smoothing functions are available:

KALCVF

performs covariance filtering and prediction

KALCVS

performs fixed-interval smoothing

Getting Started with State Space Models

Kalman Filtering Example 1: Likelihood Function Evaluation

Kalman Filtering Example 2: Estimating an SSM Using the EM Algorithm

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