DocumentCode
1327076
Title
A new order estimation technique for time series modeling
Author
Davis, Mark H A ; Zheng, Wei Xing
Author_Institution
Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
Volume
42
Issue
3
fYear
1997
fDate
3/1/1997 12:00:00 AM
Firstpage
400
Lastpage
403
Abstract
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 Davis and Fotopoulos (1991). 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
autoregressive moving average processes; modelling; parameter estimation; realisation theory; time series; approximate stochastic realization; autoregressive moving average model; order estimation technique; time series modeling; Autoregressive processes; Councils; Linear approximation; Probability; Signal processing; Signal processing algorithms; Stochastic processes; Stochastic resonance; System identification; White noise;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/9.557584
Filename
557584
Link To Document