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Record: oai:ARNO:20001

AuthorsI. Akman, J.G. de Gooijer
TitleComponent extraction analysis of multivariate time series
JournalComputational Statistics and Data Analysis
Volume21
Year1996
Pages487-499
ISSN01679473
FacultyFaculty of Economics and Business
Institute/dept.FEB: Amsterdam School of Economics Research Institute (ASE-RI)
KeywordsComponents extraction; Multivariate time series; Nonstationarity
AbstractA 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.
Document typeArticle
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