China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach
针对中国生猪存栏数据异常下降的问题，使用支持向量回归基于价格-存栏关系预测真实存栏，发现报告数据高估了下降幅度，对农业政策制定和产业分析有参考价值。
Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, support vector regression has superior forecasting performance in small sample applications. In this article, we introduce support vector regression via an application to China's hog market. Since 2014, China's hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use support vector regression to predict the true inventory based on the price‐inventory relationship before 2014. We show that, in this application with a small sample size, support vector regression outperforms neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data.