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

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.

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