A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Li Liu · Qianru Wang · Jianzhou Wang · Ming Liu

80 citations0/0 files storedComputational Intelligence

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Grey System Theory Applications Forecasting Techniques and Applications Stock Market Forecasting Methods

Abstract

Grey system theory has been widely used to forecast the economic data that are often nonlinear, irregular, and nonstationary. Current forecasting models based on grey system theory could adapt to various economic time series data. However, these models ignored the importance of the model parameter optimization and the use of recent data, which lead to poor forecasting accuracy. In this article, we propose a novel forecasting model, called particle swarm optimization rolling grey model (PSO‐RGM(1,1)), based on a rolling mechanism GM with optimized parameters by using the particle swarm optimization algorithm. The simple model is shown to be very effective in forecasting the tertiary industry data sequences, which are short and noisy but regular in secular trend. The experimental results show that PSO‐RGM(1,1) outperforms other commonly used forecasting models on three real economic data sets. Our empirical study shows that PSO is found to be the best overall algorithm to optimize the parameter of RGM compared with other well‐known metaheuristics. Furthermore, we evaluated other variant PSOs and found that single particle PSO outperforms others overall in terms of prediction accuracy, convergence speed, and degree of certainty.

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References

1982Control problems of grey systems 200625 years of time series forecasting 2009Grey system theory-based models in time series prediction 2007Grey prediction with rolling mechanism for electricity demand forecasting of Turkey 2004A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns 2003Applications of improved grey prediction model for power demand forecasting 2003Applying the Grey prediction model to the global integrated circuit industry 2001Neural network forecasting of Canadian GDP growth

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