Taking potatoes as the research object and using 72 sets of monthly price data from January 2014 to December 2019, this paper proposed an Empirical Mode Decomposition and Extreme Learning Machine agricultural product price combination prediction model. The combinatorial prediction model used the autonomous decomposition ability of Empirical Mode Decomposition and the advantage of the Extreme Learning Machine’s fewer parameters. Firstly, the original price series is decomposed into several Intrinsic Mode Functions and remainder terms by Empirical Mode Decomposition, then these components are separately predicted by Extreme Learning Machines, and finally the prediction results of each component are combined to obtain the original sequence Predicted Value.

This method is applied to the prediction of potato prices and the evaluation and analysis of their prediction results. The results showed that the average absolute error was 0.093 yuan/kg, the average percentage error is 4.265%, and the root mean square error was 0.148. We compared our results with the results obtained by using the single Extreme Learning Machine, the BP Neural Network, and the ARIMA method. The results of this comparison showed that the prediction model combined with Empirical Mode Decomposition and Extreme Learning Machine performed better at agricultural product price prediction than the other methods. The results provide a method of predicting the price fluctuation of agricultural products, and they provide a reference for the industry and government department in charge to use in guaranteeing the decision of agricultural product supply.