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):

Report Date:

1972-07-01

Pagination or Media Count:

43.0

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

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE