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Article: Recursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesis
Title | Recursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesis |
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Authors | |
Issue Date | 1988 |
Publisher | University of Lancaster, Institute of Environmental & Biological Sciences, Environmental Science Division. |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/157760 |
DC Field | Value | Language |
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dc.contributor.author | Ng, CN | en_US |
dc.date.accessioned | 2012-08-08T08:55:36Z | - |
dc.date.available | 2012-08-08T08:55:36Z | - |
dc.date.issued | 1988 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/157760 | - |
dc.description.abstract | The 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 | en_US |
dc.language | eng | en_US |
dc.publisher | University of Lancaster, Institute of Environmental & Biological Sciences, Environmental Science Division. | - |
dc.title | Recursive identification, estimation and forecasting of non- stationary time series. Ph.D. thesis | en_US |
dc.type | Article | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-85040876967 | en_US |