標題: Model identification of ARIMA family using genetic algorithms
作者: Ong, CS
Huang, JJ
Tzeng, GH
運輸與物流管理系 註:原交通所+運管所
科技管理研究所
Department of Transportation and Logistics Management
Institute of Management of Technology
關鍵字: ARIMA;stationary;SARIMA;genetic algorithms;model identification
公開日期: 25-五月-2005
摘要: ARIMA 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.
URI: http://dx.doi.org/10.1016/j.amc.2004.06.044
http://hdl.handle.net/11536/13698
ISSN: 0096-3003
DOI: 10.1016/j.amc.2004.06.044
期刊: APPLIED MATHEMATICS AND COMPUTATION
Volume: 164
Issue: 3
起始頁: 885
結束頁: 912
顯示於類別:期刊論文


文件中的檔案:

  1. 000228871500019.pdf