Research on Demand Forecasting for Cold Chain Logistics of Fresh Agricultural Products in Guangdong Province Based on ARIMA-Random Forest Model
Jingjing Xu

Guangzhou College of Commerce，Guangzhou Guangdong Province，510630;

Abstract:This article proposes an ARIMA-random forest combined model to address the demand forecasting issue of cold chain logistics for fresh agricultural products in Guangdong Province. The aim is to enhance forecasting accuracy and provide decision support for cold chain logistics planning. Using data from 1995 to 2024 on six types of fresh agricultural products in Guangdong Province, including poultry eggs, fruits, vegetables, aquatic products, milk, and meat, combined with characteristic factors such as fresh e-commerce transaction volume and policy quantity, the demand trend is captured through the ARIMA model, and the residuals are corrected using the random forest model to construct a fused model. Based on this, the demand for various products from 2025 to 2027 is predicted, and the total cold chain logistics demand is calculated by combining the low-temperature processing rate. The research can provide a scientific basis for the layout of cold chain infrastructure and policy formulation in Guangdong Province.

Key words:Cold chain logistics;Demand forecasting;Combined model;Fresh agricultural products

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