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Article: Recursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesis

TitleRecursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesis
Authors
Issue Date1988
AbstractThe thesis describes a new, fully recursive method for the identification, estimation and forecasting of non-stationary time series. This new approach is based on a step-wise decomposition of the time series data into its constituent components; and the separate identification and estimation of the signal generating models for these components. The various signal generating models are combined to yield an overall state-space model, which then provides the basis for forecasting using standard Kalman Filter (KF) methods. An "adaptive' forecasting method is subsequently developed based on these recursive estimation and forecasting procedures. -from Author
Persistent Identifierhttp://hdl.handle.net/10722/157760

 

DC FieldValueLanguage
dc.contributor.authorNg, CNen_US
dc.date.accessioned2012-08-08T08:55:36Z-
dc.date.available2012-08-08T08:55:36Z-
dc.date.issued1988en_US
dc.identifier.urihttp://hdl.handle.net/10722/157760-
dc.description.abstractThe thesis describes a new, fully recursive method for the identification, estimation and forecasting of non-stationary time series. This new approach is based on a step-wise decomposition of the time series data into its constituent components; and the separate identification and estimation of the signal generating models for these components. The various signal generating models are combined to yield an overall state-space model, which then provides the basis for forecasting using standard Kalman Filter (KF) methods. An "adaptive' forecasting method is subsequently developed based on these recursive estimation and forecasting procedures. -from Authoren_US
dc.languageengen_US
dc.titleRecursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesisen_US
dc.typeArticleen_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0024156810en_US

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