DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kun-Chang | - |
dc.contributor.author | Yang, Jin-Seol | - |
dc.contributor.author | Park, Sung Joo | - |
dc.date.accessioned | 2013-03-15T00:07:00Z | - |
dc.date.available | 2013-03-15T00:07:00Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1991 | - |
dc.identifier.citation | IJCNN'91, v., no., pp.232 - 236 | - |
dc.identifier.uri | http://hdl.handle.net/10203/114842 | - |
dc.description.abstract | The authors present a neural-network-based approach to time series modeling (TSM) in which a time series is classified into one of the autoregressive moving-average (ARMA) models. The main feature of this approach lies in extraction of regularities from the extended sample autocorrelation function (ESACF) which is derived from a given time series being considered. The role of the neural network is to recognize the ESACF patterns whose interpretation is essential for a successful TSM. The backpropagation learning algorithm is used to learn the ESACF patterns within the framework of a multilayered neural network. Through extensive computer experiments with real time series, the neural-network-based TSM proved promising due to its robust pattern-recognition ability in two aspects: it not only avoids statistical difficulties, but also provides more user-friendly decision-making aids for forecasting purposes. | - |
dc.language | ENG | - |
dc.publisher | Dept. of Manage. Inf. Syst | - |
dc.title | Neural Network-Based Time Series Modeling: ARMA Model Identification via ESACF Approach | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 232 | - |
dc.citation.endingpage | 236 | - |
dc.citation.publicationname | IJCNN'91 | - |
dc.identifier.conferencecountry | South Korea | - |
dc.identifier.conferencecountry | South Korea | - |
dc.contributor.localauthor | Park, Sung Joo | - |
dc.contributor.nonIdAuthor | Lee, Kun-Chang | - |
dc.contributor.nonIdAuthor | Yang, Jin-Seol | - |
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