Tao Xiong
ABSTRACT
We develop a model to forecast Chinese soybean futures price with eighteen predictors by integrating the recently proposed dynamic model averaging (DMA) and particle swarm optimization (PSO). Specifically, three important parameters, i.e., two forgetting factors and a decay factor, of DMA are tuned by PSO. The proposed prediction model, named DMA-PSO, not only allow for coefficients to change over time, but also allow for forecasting model to evolve over time. Experimental results show that the proposed DMA-PSO outperforms four counterparts and the best predictors in DMA-PSO for forecasting soybean futures price vary a lot over time1.
- A. E. Raftery, M. Kárný and P. Ettler, 2010. Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics 52(1), pp. 52--66.Google Scholar
Cross Ref
- J. Kennedy, R. Eberhart, 1995, Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942--1948.Google ScholarCross Ref
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