Analyzing U.S. Maritime Trade and COVID-19 Impact Using Machine Learning

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Author(s): Peter R. Abraldes (Pennsylvania State University, USA), James Rotella (Pennsylvania State University, USA), Partha Mukherjee (Pennsylvania State University, USA)and Youakim Badr (Pennsylvania State University, USA)
Copyright: 2023
Pages: 19
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch021

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

International trade is crucial to the U.S. economy. When measured by metric tonnage, 75% of the volume is transported by sea. This work explores the variables that impact trade volume, both on the individual and aggregate shipment level. Three supervised machine-learning techniques use data about U.S. maritime trade, U.S. unemployment, international tariff rates, and country exchange rates for the period of 2010 through 2019. The same variables are then used in combination with new information specific to COVID-19 for the period of 2019 and 2020. The tonnage of individual shipments remains unchanged before and after the prevalence of COVID-19. However, the frequency of shipments decreases during 2020, resulting in an aggregate decline of trade. Metrics that measure COVID-19's impact produce better model prediction accuracy.

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Analyzing U.S. Maritime Trade and COVID-19 Impact Using Machine Learning

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