Improving the prediction accuracy of agricultural product futures prices is important for the investors, agricultural producers and policy makers. This is to evade the risks and enable the government departments to formulate appropriate agricultural regulations and policies. This study employs Ensemble Empirical Mode Decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model were then compared with the benchmark individual models, SVM, NN, and ARIMA. Our main interest in this study is on the short-term forecasting, and thus we only consider 1-day and 3-days forecast horizons. The results indicated that the prediction performance of EEMD combined model is better than that of individual models, especially for the 3-days forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods to forecast high-frequency volatile components. However, there is no obvious difference between individual models in predicting the low-frequency components.
Vegetable is one of the necessities of people's daily life. The quality and safety of vegetable is associated directly with the health of citizen and affects the sustainable development of national economy. It is Pesticide residue that is one of the important factors which affects the quality and safety of vegetable. In this paper, Logistic model of data mining technology is applied to mining the limited assay information of vegetable and forecasting the risk of pesticide residue in the further. The result is credible and can offer some decisions based on the supervision of vegetable quality and safety to the supervisors.
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