Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Analysis and Modeling for Cold Chain Logistics of Agricultural Products Demand Forecasting

Version 1 : Received: 18 January 2021 / Approved: 19 January 2021 / Online: 19 January 2021 (11:50:09 CET)

How to cite: Wen, S.; Li, J.; Wang, R.; Liu, H. Analysis and Modeling for Cold Chain Logistics of Agricultural Products Demand Forecasting. Preprints 2021, 2021010375 (doi: 10.20944/preprints202101.0375.v1). Wen, S.; Li, J.; Wang, R.; Liu, H. Analysis and Modeling for Cold Chain Logistics of Agricultural Products Demand Forecasting. Preprints 2021, 2021010375 (doi: 10.20944/preprints202101.0375.v1).

Abstract

Cold chain logistics of Agricultural Products demand forecasting can provide the scientific basis for the country to formulate logistics strategy, which further promotes the development of social economy and the improvement of living standards in China. In this paper, a new mathematical combined model is proposed to Agricultural Products Demand. Shandong, one of a China’s province, serves as the main producer and distributor of agricultural products. Based on the index system created from multiple related factors influencing cold chain logistics demand of agricultural products in Shandong, this paper employs principal component analysis to reduce the dimension of various indexes and predicts principal components with time series. Thereafter, multiple linear regression model and neural network model were constructed to forecast the cold chain logistics demand of agricultural products in Shandong, and their combined forecast models were compared. What's more, the paper provides insight for reference and decision-making concerning the development of cold chain logistics industry of agricultural products in Shandong province.

Subject Areas

cold chain logistics of agricultural products; demand forecast; principal component analysis, multiple linear regression, neural network.

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