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