A reduced parameter bilinear time series model

A new bilinear time series structure is proposed and is tested on three sample time series to demonstrate its effectiveness. It is found that the proposed bilinear model can represent both nonlinearity and multiperiodicity, and it therefore provides a useful model class for general applications. In...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 42; no. 7; pp. 1867 - 1870
Main Authors Yongqing Zhang, Hagan, M.T.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1994
Institute of Electrical and Electronics Engineers
Subjects
Analysis of variance
Applied sciences
Autoregressive processes
Convolution
Digital signal processing
Exact sciences and technology
Gaussian noise
Information, signal and communications theory
Integrated circuit modeling
Mathematical methods
Signal analysis
Signal processing
Telecommunications
Telecommunications and information theory
White noise
Theoretical study
Autoregressive moving average
Signal processing
Bilinear system
Modeling
Time series
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Summary:A new bilinear time series structure is proposed and is tested on three sample time series to demonstrate its effectiveness. It is found that the proposed bilinear model can represent both nonlinearity and multiperiodicity, and it therefore provides a useful model class for general applications. In addition, the proposed bilinear model uses fewer parameters than conventional bilinear models with the same structure.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
1941-0476
DOI:10.1109/78.298301