A new order estimation technique for time series modeling

Mark H.A. Davis; Wei Xing Zheng

doi:10.1109/9.557584

A new order estimation technique for time series modeling

Mark H.A. Davis, Wei Xing Zheng

School of Computer, Data & Mathematical Sciences

Mitsubishi Finance International Plc

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

In this paper, a new approach to estimating the order of the autoregressive moving average model is proposed, which is based on the approximate stochastic realization introduced in [7]. The present approach is attractive because overparameterization - a very common problem in order determination - is avoided successfully. Simulation results are included to illustrate the effectiveness of the proposed order estimation approach.

Original language	English
Pages (from-to)	400-403
Number of pages	4
Journal	IEEE Transactions on Automatic Control
Volume	42
Issue number	3
DOIs	https://doi.org/10.1109/9.557584
Publication status	Published - 1997

Keywords

ARMA model
Order estimation
Stochastic realization
Time series modeling

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10.1109/9.557584

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title = "A new order estimation technique for time series modeling",

abstract = "In this paper, a new approach to estimating the order of the autoregressive moving average model is proposed, which is based on the approximate stochastic realization introduced in [7]. The present approach is attractive because overparameterization - a very common problem in order determination - is avoided successfully. Simulation results are included to illustrate the effectiveness of the proposed order estimation approach.",

keywords = "ARMA model, Order estimation, Stochastic realization, Time series modeling",

author = "Davis, {Mark H.A.} and Zheng, {Wei Xing}",

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T1 - A new order estimation technique for time series modeling

AU - Davis, Mark H.A.

AU - Zheng, Wei Xing

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AB - In this paper, a new approach to estimating the order of the autoregressive moving average model is proposed, which is based on the approximate stochastic realization introduced in [7]. The present approach is attractive because overparameterization - a very common problem in order determination - is avoided successfully. Simulation results are included to illustrate the effectiveness of the proposed order estimation approach.

KW - ARMA model

KW - Order estimation

KW - Stochastic realization

KW - Time series modeling

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U2 - 10.1109/9.557584

DO - 10.1109/9.557584

M3 - Article

AN - SCOPUS:0031100304

SN - 0018-9286

VL - 42

SP - 400

EP - 403

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

IS - 3

ER -

A new order estimation technique for time series modeling

Abstract

Keywords

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