Abstract
In order to predict pork prices accurately, a pork price forecast model based on support vector machine (SVM) is proposed. Data of 229 nationwide weekly average retail pork prices from January 1, 2008 to July 1, 2012 is selected as samples to train and test SVM. The experiment results show that the relative error is kept in the range between −0.04 and 0.04, the mean squared error is 0.00157 and correlation coefficient is 99.5196 %. This method can also provide a reference for predicting other agricultural prices.
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Acknowledgements
The 948 program of MoA (No.2012-Z1) Special program, This work is supported by the Key Technologies R&D Program of China during the 12th Five-Year Plan period (2012BAH20B04), and National Natural Science Foundation of China (No. 61003263).
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Zhang, JH., Li, ZM., Kong, FT., Dong, XX., Chen, W., Wang, SW. (2014). Prediction of Pork Prices Based on SVM. In: Xu, S. (eds) Proceedings of 2013 World Agricultural Outlook Conference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54389-0_15
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DOI: https://doi.org/10.1007/978-3-642-54389-0_15
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