Abstract: In response to the nonlinear characteristics of agricultural product price data, a price prediction model for agricultural products based on the EMD-PSO-ARIMA model was proposed. Firstly, the EMD algorithm was used to eliminate the instability of price data,secondly, the PSO algorithm was applied to optimize the lag parameters of the ARIMA model and predict the sequence after decomposing the original data,finally, multiple predicted values were accumulated to obtain the final result. Empirical research was conducted on the price data of bulb crops （using garlic as an example）, rhizome crops （using potatoes as an example）, and leafy vegetables （using cabbage as an example） at a farmer’s market in Henan Province from January 2004 to December 2021. The RMSE of the EMD-PSO-ARIMA model for predicting prices of garlic, potatoes, and cabbage was 0.029 5, 0.016 8, and 0.066 9, respectively,MAE was 0.027 4, 0.018 9, 0.059 8, respectively, and MAPE were 0.32%, 0.64%, and 2.54%, respectively;compared with ARIAM, PSO-ARIMA, and EMD-ARIMA models, the three evaluation indicators of the EMD-PSO-ARIMA model had all decreased to varying degrees, and the model had the highest prediction accuracy. The EMD-PSO-ARIMA model could effectively make accurate predictions on the prices of three agricultural products, improving the predictive performance of the model to a certain extent. It could provide decision support for agricultural producers, operators, and governments, and maintain the stability of the agricultural market.

Key words: EMD-PSO-ARIMA model, agricultural product price, prediction