Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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.