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Some recent advances in time series modeling

Parzen, E.·1974·IEEE Transactions on Automatic Control

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 functiong_{\infty}is developed, and the time series modeling problem is defined to be the estimation ofg_{\infty}. Section III reviews estimation theory. Section IV describes autoregressive estimators ofg_{\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.

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Metadata

Authors
Parzen, E.
Venue
IEEE Transactions on Automatic Control
Published
1974-12
Volume
19
Issue
6
Pages
723-730
ISSN
0018-9286 (ISSN-L)
OpenAlex
W2117418380
Cited by
341
References
0
FWCI
27.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Some recent advances in time series modeling. https://app.cochat.ai/papers/10.1109/tac.1974.1100733
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Parzen, E. (1974). Some recent advances in time series modeling. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.1974.1100733

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.