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

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

Peter R. Abraldes, James Rotella, Partha Mukherjee, Youakim Badr
Copyright: © 2023 |Pages: 19
ISBN13: 9781799892205|ISBN10: 1799892204|EISBN13: 9781799892212
DOI: 10.4018/978-1-7998-9220-5.ch021
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MLA

Abraldes, Peter R., et al. "Analyzing U.S. Maritime Trade and COVID-19 Impact Using Machine Learning." Encyclopedia of Data Science and Machine Learning, edited by John Wang, IGI Global, 2023, pp. 323-341. https://doi.org/10.4018/978-1-7998-9220-5.ch021

APA

Abraldes, P. R., Rotella, J., Mukherjee, P., & Badr, Y. (2023). Analyzing U.S. Maritime Trade and COVID-19 Impact Using Machine Learning. In J. Wang (Ed.), Encyclopedia of Data Science and Machine Learning (pp. 323-341). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch021

Chicago

Abraldes, Peter R., et al. "Analyzing U.S. Maritime Trade and COVID-19 Impact Using Machine Learning." In Encyclopedia of Data Science and Machine Learning, edited by John Wang, 323-341. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-7998-9220-5.ch021

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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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