Dynamic grey platform for efficient forecasting management

In this paper, we propose a dynamic grey platform to modify the traditional algorithms by applying two new prediction algorithms for forecasting management. The proposed platform integrates a grey model (GM) with an exponentially weighted moving average EWMA controller known as the EGM model. The EGM model attempts to improve the forecast accuracy and efficiency. The prediction error of the EGM model is minimized by applying a dynamic genetic algorithm (DGA). The contributions of the DGA are essentially from its two features: (1) the crossover and mutation rate controller of GA parameter optimization; and (2) the variable controller of EGM background value optimization. Six benchmarking data sets have been used in simulation to evaluate the effectiveness of our proposed model. The experimental results reveal that the better prediction accuracy reduces the cost of Taiwan's green gross domestic product (GDP).