Authors: Guo-Dong Li, Daisuke Yamaguchi, Masatake Nagai, Shiro Masuda
Addresses: Department of System Design, Management Systems Engineering, Tokyo Metropolitan University, 191-0065 Hino Tokyo, Japan. ' Graduate School of Natural Science and Technology, Okayama University, Okayama City 700-8530, Japan. ' Department of Engineering, Kanagawa University, Yokohama City 221-8686, Japan. ' Department of System Design Tokyo Metropolitan University, Hino City 191-0065, Japan
Abstract: In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1) model. This combined model takes the advantage of high predictable power of GM(1,1) model and at the same time takes the advantage of the prediction power of ARIMA model. For prediction accuracy improvements, three-points average model is further applied to ARGM(1,1) model. We call the improved version as 3P-ARGM(1,1) model. In addition, Markov chain model from stochastic process theory to enhance the prediction power of 3P-ARGM(1,1) model. The generated model is called as M3P-ARGM(1,1) model. As an illustrative example, we use the statistical data recording the number of China|s fixed telephone subscribers from 1989 to 2004 for a validation of the effectiveness of the M3P-ARGM(1,1) model.
Keywords: telecommunications industry development; grey differential equation model; GM(1,1) model; autoregressive integrated moving average; ARIMA models; three-points average; Markov chain modelling; prediction analysis; time series; China.
International Journal of Learning and Change, 2008 Vol.3 No.1, pp.92 - 109
Published online: 20 Jun 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article