Grain Price Forecasting Using a Hybrid Stochastic Method
Yu Zhao (),
Zhongshun Shi () and
Lei He ()
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Yu Zhao: Department of Industrial Engineering and Management, Peking University, Beijing 100871, P. R. China
Xi Zhang: Department of Industrial Engineering and Management, Peking University, Beijing 100871, P. R. China
Zhongshun Shi: Department of Industrial and Systems Engineering, University of Wisconsin Madison, Madison, WI 53706, USA
Lei He: Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA
Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 05, 1-24
Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.
Keywords: Stochastic algorithm; neural networks; wavelet decomposition; price forecasting; stochastic process (search for similar items in EconPapers)
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