Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

Abstract:

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

• Periodical: Applied Mechanics and Materials (Volumes 644-650)
• Pages: 2636-2640
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.644-650.2636
• Online since: September 2014
• Authors: Jian Hua Zhang, Fan Tao Kong*, Jian Zhai Wu, Meng Shuai Zhu, Ke Xu, Jia Jia Liu
• Keywords: Prediction Model, Price, Time Series, Tomato, Wavelet Neural Network

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DOI: 10.1016/j.aaspro.2010.09.049