Machine learning and neural network based model predictions of soybean export shares from US Gulf to China. (7th September 2022)

• Record Type: Journal Article
• Authors: Awasthi, Shantanu; SenGupta, Indranil; Wilson, William; Lakkakula, Prithviraj
• Is Part Of: Statistical analysis and data mining. Volume 15:Number 6(2022)
• Page Start: 707
• Page End: 721
• Publication Date: 2022-09-07
• Subjects: export shares -- first exit time -- random forest -- recurrent neural networks -- soybean; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11595
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: In this paper, we propose a general model for the soybean export market share dynamics and provide several theoretical analyses related to a special case of the general model. We implement machine and neural network algorithms to train, analyze, and predict US Gulf soybean market shares (target variable) to China using weekly time series data consisting of several features between January 6, 2012 and January 3, 2020. Overall, the results indicate that US Gulf soybean market shares to China are volatile and can be effectively explained (predicted) using a set of logical input variables. Some of the variables, including shipments due at US Gulf port in 10 days, cost of transporting soybean shipments via barge at Mid‐Mississippi, and soybean exports loaded at US Gulf port in the past 7 days, and binary variables have shown significant influence in predicting soybean market shares.