Forecast Analysis of Cold Chain Logistics Demand Based on Different Models

Abstract

With economic development and the improvement of residents' consumption capacity, public awareness of pursuing healthy lifestyles has been continuously strengthened, leading to rising expectations for the variety and quality of fresh food products. Cold chain logistics, as a key link in ensuring the circulation quality of fresh products, has gained increasing importance. Henan Province, a major agricultural base in China, boasts abundant production of fresh agricultural products but suffers significant losses during circulation, largely due to an underdeveloped cold chain logistics system. Although Henan possesses advantages in output and geographical location for developing cold chain logistics for fresh agricultural products, its current development still faces multiple challenges. Based on an analysis of the development status of cold chain logistics for fresh agricultural products in Henan Province, this paper constructs five forecasting models: Wavelet Neural Network, BP Neural Network, GA-BP Neural Network, PSO-BP Neural Network, and LSTM. After comparing their prediction accuracy, the LSTM model with the best performance is selected to forecast the demand for cold chain logistics of fresh agricultural products in Henan, providing a decision-making basis for optimizing the allocation of regional cold chain logistics resources. Based on the forecast results, this paper further proposes countermeasures to promote the high-quality development of fresh agricultural product cold chain logistics in Henan Province, aiming to balance supply and demand, alleviate operational pressure, and drive the industry towards greater efficiency, sustainability, and intelligence.