A Novel Method to Improve the Accuracy of Grey Prediction Model in Economic Prediction

Abstract:

Grey system theory has been widely used to forecast the economic data that are often highly nonlinear, irregular and non-stationary. Many models based on grey system theory could adapt to various economic time series data. However, some of these models didnt consider the impact of the model parameters, or only considered a simple change of the model parameters for the prediction. In this paper, we proposed the PSO based GM (1, 1) model using the optimized parameters in order to improve the forecasting accuracy. The experiment shows that PSO based GM (1, 1) gets much better forecasting accuracy compared with other widely used grey models on the actual chaotic economic data.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 3331-3335
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.3331
• Online since: August 2013
• Authors: Qian Ru Wang, Xi Wei Chen, Da Shi Luo, Yu Feng Wei, Li Ya Jin, Li Liu
• Keywords: Grey Prediction Model, Particle Swarm Optimization (PSO), Rolling Mechanism

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