Component extraction analysis of multivariate time series

Date

1996-05-01

Author

Akman, I
DeGooijer, JG

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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A method for modelling several observed parallel time series is proposed. The method involves seeking possible common underlying pure AR and MA components in the series. The common components are forced to be mutually uncorrelated so that univariate time series modelling and forecasting techniques can be applied. The proposed method is shown to be a useful addition to the time series analyst's toolkit, if common sources of variation in multivariate data need to be quickly identified.

Subject Keywords

Components extraction, Multivariate time series, Nonstationarity, Nonstationarity

URI

https://hdl.handle.net/11511/65320

Journal

COMPUTATIONAL STATISTICS & DATA ANALYSIS

DOI

https://doi.org/10.1016/0167-9473(95)00031-3

Collections

Department of Computer Engineering, Article

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Citation Formats

I. Akman and J. DeGooijer, “Component extraction analysis of multivariate time series,” COMPUTATIONAL STATISTICS & DATA ANALYSIS, pp. 487–499, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65320.