sci-文章

Hsu (2003) pointed out that the product life-cycle in high-tech industry is short. Moreover, high technical requirements, high market risk and access to technology are costly. With limited data, the traditional forecasting models are applicable in high-tech industries. Therefore, this study uses grey theory, which was proposed in 1982 (Deng, 1982), as the system mode of unclear and incomplete information to build a grey model for prediction and decision-making. The general form of a grey forecasting model is GM(n,h). It contains a group of differential equations, where n denotes the order of the ordinary differential equation of the grey model and h represents the number of the variables. GM(1,1) (i.e., first-order differential equation, one variable grey forecasting model) is the most commonly used grey forecasting model. In recent years, it has been widely applied to various research fields and has achieved good prediction accuracy. The applications of this model include electricity demand forecasting (Hsu & Chen, 2003), stock market forecasting (Wang, 2002), tourism (Yu & Schwartz, 2006), and hi-tech industries such as the integrated circuit industry (Chang, Lai, & Yu, 2005; Hsu, 2003; Wang & Hsu, 2008), optoelectronics industry (Lin & Yang, 2003) and TFT-LCD industry
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
In the social sciences, people often establish models, and use information to predict outcomes (Hillier & Lieberman, 1995). Superior forecasting ability is an important characteristic of successful managers in complex and uncertain environments. In a changing society with rapid developments in science and technology, by getting more information in advanced, managers can better understand the future of situations and build the right strategies and plans, so that they can maximize profits (Drucker, 1980). Thus, many forecasting methods have been developed over the years, including the linear regression model, Markov methods, time series model, the causal method, etc. (Chan, 1993; Chu, 1998). Since these methods are fast, easy to use and accurate, they have been used in a wide range of applications. However, these methods have limitations. For example, the linear regression model assumes that variables are independent and that samples have normal distribution. This model, therefore, requires a larger number of samples. The Markov method must know the transition probability between each state. The time series model requires stable trends and patterns of historical data. The causal method must have enough historical data and a sufficient amount of analysis. Only then can it clearly express events that have been predicted among the relevant factors and the other factors of the relationship, and then apply its causal relationship for the purpose of prediction.
Expert Systems with Applications 38 (2011) 13879–13885
Contents lists available at ScienceDirect
Expert Systems with Applications
journal homepage: /locate/eswa
Using improved grey forecasting models to forecast the output of opto-electronics industry
Li-Chang Hsu ⇑
Department of Finance, Ling Tung University, 1 Ling Tung Road, Nantun, Taichung 40852, Taiwan, ROC
However, in real world cases, the above forecasting methods are often unable to obtain a large amount of data and cannot meet these assumptions. As result, in such a case, their prediction accuracy is not very high. Moreover, managers are often faced with unclear information packets because they do not have information for forecasting and decision-making. Therefore, future research will attempt to find solutions in the shortest possible time and construct good predictive models with a small unt of data.
⇑ Tel.: +886 4 2389 2088x3642; fax: +886 4 2389 5293.
E-mail address: ltctht87@.tw
0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.04.192
article info
Keywords: Forecasting Opto-electronics industry Genetic algorithm GM(1,1) Rolling GM(1,1) Transformed GM(1,1)
abstract
Numerous forecasting models have been developed. Each has its own conditions of application. However, it has always been an important research objective to improve prediction accuracy with a small amount of data. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting models still have some potential problems that need to be improved. Therefore, this study proposed an improved transformed grey model based on a genetic algorithm (ITGM(1,1)), and used the output of the opto-electronics industry in Taiwan from 1990 to 2008 as an example for verification. Three grey forecasting models, GM(1,1), rolling GM(1,1), and the transformed GM(1,1), were chosen for the purpose of comparison with ITGM(1,1) by mean absolute percent error and root mean square percent error. The results show that ITGM(1,1) is more accurate than the other three models in both in-sample and out-of-sample forecasting performance, and can greatly improve the accuracy of short-term forecasts.