Time-series analysis with a hybrid Box-Jenkins ARIMA and neural network model
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades.More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model's unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
作 者： Dilli R Aryal 王要武 作者单位： School of Management,Harbin Institute of Teehnology,Harbin,150001,China 刊 名： 哈尔滨工业大学学报（英文版） EI 英文刊名： JOURNAL OF HARBIN INSTITUTE OF TECHNOLOGY(NEW SERIES) 年，卷(期)： 2004 11(4) 分类号： C931 关键词： time series analysis ABIMA Box-Jenkins methodology artificial neural networks hybrid model