Accession Number : AD0757573

Title :   Topics in Control. 1. State Variable Approach to Time Series Representation and Forecasting.

Descriptive Note : Technical rept.,

Corporate Author : WISCONSIN UNIV MADISON DEPT OF STATISTICS

Personal Author(s) : MacGregor,John F.

Report Date : JUL 1972

Pagination or Media Count : 43

Abstract : The state variable approach to modelling discrete linear dynamic-stochastic systems is discussed and related to that using transfer function and autoregressive-integrated-moving-average (ARIMA) models. It is shown that the standard form of the state variable model using two independent Gaussian noise vectors which is used extensively in the literature is not a parsimonious representation (i.e., one that is efficient in its use of parameters) but that it can always be written in a more parsimonious form employing a single Gaussian noise vector. Several such parsimonious state representations are given for the general transfer function-ARIMA model. The Kalman filter for estimating the state vector is derived using a Bayesian argument and its use in time series forecasting and its relationship to recursive least squares are discussed. (Author Modified Abstract)

Descriptors :   (*ADAPTIVE CONTROL SYSTEMS, MATHEMATICAL MODELS), LINEAR SYSTEMS, STOCHASTIC PROCESSES, TIME SERIES ANALYSIS, TRANSFER FUNCTIONS, WHITE NOISE, CORRELATION TECHNIQUES

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE