Some recent advances in time series modeling

Author

Parzen, Emanuel

Author_Institution

State University of New York, Buffalo, NY, USA

Volume

19

Issue

6

fYear

1974

fDate

12/1/1974 12:00:00 AM

Firstpage

723

Lastpage

730

Abstract

The aim of this paper is to describe some of the important concepts and techniques which seem to help provide a solution of the stationary time series problem (prediction and model identification). Section I reviews models. Section II reviews prediction theory and develops criteria of closeness of a "fitted" model to a "true" model. The central role of the infinite autoregressive transfer function $g_{\\infty }$ is developed, and the time series modeling problem is defined to be the estimation of $g_{\\infty }$ . Section III reviews estimation theory. Section IV describes autoregressive estimators of $g_{\\infty }$ . It introduces a criterion for selecting the Order of an autoregressive estimator which can be regarded as determining the order of an AR scheme when in fact the time series is generated by an AR scheme of finite order.

Keywords

Autoregressive processes; Moving-average processes; Parameter estimation; Prediction methods; Time series; Character generation; Estimation theory; Helium; Parameter estimation; Prediction theory; Predictive models; Signal analysis; Signal generators; Time series analysis; Transfer functions;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.1974.1100733

Filename

1100733