Agricultural Product Price Prediction Based on Empirical Mode Decomposition and Graph Neural Network Algorithm
To improve the accuracy of agricultural product price prediced by using graph neural network(GNN)algo-rithm,empirical mode decomposition(EMD)method was used to extract the historical price signals of agricultural pro-ducts in turn according to time slices,so as to extract the characteristics of the historical price signals.The original price signal was decomposed into several instrinsic mode functions and residual terms,and the sample characteristics were con-structed according to the instrinsic mode functions.According to the obtained sample characteristics,the graph structure was constructed,and the characteristic signals output from the graph structure pass through the transition function and prediction function of GNN algorithm,and the agricultural product price prediction results were output by continuously reducing the loss value.The results show that EMD can effectively decompose and extract the intrinsic mode function components of the original agricultural product price signals,thus the average absolute error of agricultural product price predicted by using EMD-GNN algorithm is reduced by 71.4%.Compared with other types of forecasting algorithms,the average absolute error of EMD-GNN algorithm for the price prediction of 4 types of agriculture products is smaller,and the maximum value is only 2.465.
price prediction of agricultural productsgraph neural networkempirical mode decompositionintrinsic mode function
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