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dc.contributor.authorOng, CSen_US
dc.contributor.authorHuang, JJen_US
dc.contributor.authorTzeng, GHen_US
dc.date.accessioned2014-12-08T15:19:07Z-
dc.date.available2014-12-08T15:19:07Z-
dc.date.issued2005-05-25en_US
dc.identifier.issn0096-3003en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.amc.2004.06.044en_US
dc.identifier.urihttp://hdl.handle.net/11536/13698-
dc.description.abstractARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model estimation and model checking, of which model identification is the most crucial stage in building ARIMA models. However there is no method suitable for both ARIMA and SARIMA that can overcome the problem of local optima. In this paper, we provide a genetic algorithms (GA) based model identification to overcome the problem of local optima, which is suitable for any ARIMA model. Three examples of times series data sets are used for testing the effectiveness of GA, together with a real case of DRAM price forecasting to illustrate an application in the semiconductor industry. The results show that the GA-based model identification method can present better solutions, and is suitable for any ARIMA models. (c) 2004 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectARIMAen_US
dc.subjectstationaryen_US
dc.subjectSARIMAen_US
dc.subjectgenetic algorithmsen_US
dc.subjectmodel identificationen_US
dc.titleModel identification of ARIMA family using genetic algorithmsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.amc.2004.06.044en_US
dc.identifier.journalAPPLIED MATHEMATICS AND COMPUTATIONen_US
dc.citation.volume164en_US
dc.citation.issue3en_US
dc.citation.spage885en_US
dc.citation.epage912en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000228871500019-
dc.citation.woscount46-
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