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本文结合前人研究和国外经验,以大豆为例,深入研究粮食价格预测预警机制。主要利用机器学习的Lasso方法及神经网络理论,结合灰色预测进行粮食价格预测的优化,结果表明预测精度高,模型可以很好地拟合大豆价格变动情况。在此基础上,引用控制图理论,结合预测价格,构建了一个有效的价格预测预警机制。本文建立对大豆价格预测预警机制具有可行性,一定程度上可以推广到其他主要粮食价格的预测预警研究中,可为保障我国粮食安全,保护农民的利益,保证粮食市场的稳定发展,为政府制定价格干预政策提供一定的借鉴。
Based on the previous researches and overseas experiences, taking soybean as an example, this paper deeply studies the forecasting and warning mechanism of food prices. Mainly using machine learning Lasso method and neural network theory, combined with gray forecast to optimize food price forecast, the results show that the prediction accuracy is high, the model can well fit the soybean price changes. On the basis of this, we use the control chart theory and the forecast price to construct an effective price forecasting and warning system. This paper establishes the feasibility of soybean forecasting and early warning mechanism, to some extent, can be extended to other major grain prices in the prediction of early warning research, for the protection of China’s food security, protect the interests of farmers and ensure the stable development of the grain market, formulated for the government Price intervention policy to provide some reference.