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dc.contributor.authorLee, Yi-Shianen_US
dc.contributor.authorTong, Lee-Ingen_US
dc.date.accessioned2014-12-08T15:37:36Z-
dc.date.available2014-12-08T15:37:36Z-
dc.date.issued2011-02-01en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2010.07.006en_US
dc.identifier.urihttp://hdl.handle.net/11536/25853-
dc.description.abstractThe autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model. (C) 2010 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectARIMAen_US
dc.subjectHybrid modelen_US
dc.subjectGenetic programmingen_US
dc.subjectForecastingen_US
dc.subjectArtificial neural networken_US
dc.titleForecasting time series using a methodology based on autoregressive integrated moving average and genetic programmingen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.knosys.2010.07.006en_US
dc.identifier.journalKNOWLEDGE-BASED SYSTEMSen_US
dc.citation.volume24en_US
dc.citation.issue1en_US
dc.citation.spage66en_US
dc.citation.epage72en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000284344800008-
dc.citation.woscount29-
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