Improving project-profit prediction using a two-stage forecasting system

Authors:
Ping-Teng Chang;Lung-Ting Hung;Ping-Feng Pai;Kuo-Ping Lin
Affiliations:
Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung 407, Taiwan;Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung 407, Taiwan;Department of Information Management, National Chi Nan University, University Rd., Puli, Nantou 545, Taiwan;Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan
Venue:
Computers and Industrial Engineering
Year:
2013

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Abstract

Accurate project-profit prediction is a crucial issue because it can provide an early feasibility estimate for the project. In order to achieve accurate project-profit prediction, this study developed a novel two-stage forecasting system. In stage one, the proposed forecasting system adopts fuzzy clustering technology, fuzzy c-means (FCM) and kernel fuzzy c-means (KFCM), for the correct grouping of different projects. In stage two, least-squares support vector regression (LSSVR) technology is employed for forecasting the project-profit in different project groups, respectively. Moreover, genetic algorithms (GA) were simultaneously used to select the parameters of the LSSVR. The project data come from a real enterprise in Taiwan. In this study, some forecasting methodologies are also compared, for instance Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Networks (RBFNN), and Back Propagation Neural Network (BPNN), to predict project-profit in this real case. Empirical results indicate that the two-stage forecasting system (FCM+LSSVR and KFCM+LSSVR) has superior performance in terms of forecasting accuracy, compared to other methods. Furthermore, in observing the results of the two-stage forecasting system, it can be seen that FCM+LSSVR can achieve superior performance, and KFCM+LSSVR can achieve consistently good performance. Therefore, based on the empirical results, the two-stage forecasting system was verified to efficiently provide credible predictions for project-profit forecasting.