Prediction of Agricultural Products Cold Chain Logistics Demand in Sichuan Based on Metabolic GM (1, 1) Model

Abstract

In order to ensure the coordinated and rapid development of cold chain logistics industry of agricultural products, alleviate structural contradictions and provide data support for decision-making, it is necessary to carry out demand forecasting research on cold chain logistics of agricultural products. In order to improve the accuracy of demand forecasting, the traditional GM(1,1)model, new information GM(1,1)model, metabolic GM(1,1)model are established first, the cold chain logistics data of agricultural products in Sichuan Province from 2010 to 2019 are selected as panel data, and the cold chain logistics demand of agricultural products in Sichuan Province from 2016 to 2019 is simulated. Then the average relative error, the sum of error squares and the prediction accuracy of the three prediction models are solved respectively. Finally, it is found that the metabolic prediction accuracy is the highest and the prediction error is the smallest, which is more suitable for the cold chain logistics demand prediction of agricultural products.