The Prediction of Soybean Price in China Based on a Mixed Data Sampling–Support Vector Regression Model

Soybean is a crucial economic crop and it is one of the most marketized and internationalized bulk agricultural products in China. As fluctuations in soybean prices directly impact national food security and agrarian stability, it is essential to predict this price accurately. Soybean price is influenced by multiple factors, such as macroeconomic data (typically low-frequency, measured quarterly or monthly), weather conditions, and investor sentiment data (high-frequency, for example, daily). In order to incorporate mixed-frequency data into a forecasting model, the Mixed Data Sampling (MIDAS) model was employed. Given the complexity and nonlinearity of soybean price fluctuations, machine learning techniques were adopted. Therefore, a MIDAS-SVR model (combining the MIDAS model and support vector regression) is proposed in this paper, which can capture the nonlinear and non-stationary patterns of soybean prices. Data on the soybean price in China (January 2012–January 2024) were analyzed and the mean absolute percentage error (MAPE) of the MIDAS-SVR model was 1.71%, which demonstrates that the MIDAS-SVR model proposed in this paper is effective. However, this study is limited to a single time series, and further validation across diverse datasets is needed to confirm generalizability.