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2005

Model identification of ARIMA family using genetic algorithms

9 years 9 months ago
Model identification of ARIMA family using genetic algorithms
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.
Chorng-Shyong Ong, Jih-Jeng Huang, Gwo-Hshiung Tze
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where AMC
Authors Chorng-Shyong Ong, Jih-Jeng Huang, Gwo-Hshiung Tzeng
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