Ong, Chorng-ShyongChorng-ShyongOngHuang, Jih-JengJih-JengHuangTzeng, Gwo-HshiungGwo-HshiungTzeng2008-10-222018-06-292008-10-222018-06-29200500963003http://ntur.lib.ntu.edu.tw//handle/246246/84973https://www.scopus.com/inward/record.uri?eid=2-s2.0-16344380274&doi=10.1016%2fj.amc.2004.06.044&partnerID=40&md5=1a3b5770bc743dc4d3e0e657fa2dab7fARIMA 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. © 2004 Elsevier Inc. All rights reserved.application/pdf352587 bytesapplication/pdfen-USARIMA; Genetic algorithms; Model identification; SARIMA; StationaryData acquisition; Functions; Genetic algorithms; Mathematical models; Time series analysis; ARIMA; Model identification; SARIMA; Stationary; Time series data; Data reductionjournal article2-s2.0-16344380274http://ntur.lib.ntu.edu.tw/bitstream/246246/84973/1/12.pdf