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Time Series Analysis and Control Examples |

This section describes a collection of Kalman filtering and smoothing subroutines for time series analysis; immediately following are three examples using Kalman filtering subroutines. 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 measurement (or observation) equation can be written

The transition (or state) equation is denoted as a first-order Markov process of the state vector.

The following IML Kalman filter calls are supported:

- KALCVF
- performs covariance filtering and prediction
- KALCVS
- performs fixed-interval smoothing
- KALDFF
- performs diffuse covariance filtering and prediction
- KALDFS
- performs diffuse fixed-interval smoothing

The KALDFF call produces one-step prediction of the state and the unobserved random vector as well as their covariance matrices. The KALDFS call computes the smoothed estimate and its covariance matrix .

See Chapter 17, "Language Reference," for more information about Kalman filtering subroutines.

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