Title: Teaching time series and regression analysis using ports of Los Angeles and Long Beach data

Authors: Ardavan Asef-Vaziri

Addresses: David Nazarian College of Business and Economics, California State University, Northridge. 18111 Nordhoff Street, Northridge, CA 91330-824, USA

Abstract: The combined ports of Los Angeles and Long Beach (LA/LB) ports are among the world's top ten busiest container ports. Approximately 1/3 of US waterborne containers move through the LA/LB ports. The data on the volume of containerised activities in these ports provide an excellent dataset to teach time series and regression analysis. We use 26 years of data on these ports' activities to teach moving averages, exponential smoothing, trend-adjusted exponential smoothing, and regression analysis. We also use 312 monthly data for teaching seasonality-enhanced regression, multivariate seasonality regression using dummy variables, and trend and seasonality-adjusted exponential smoothing. This manuscript can be used as teaching material, or as a case study in a business analytics foundations or a supply chain management course. A set of useful Excel functions and formulas have been brought together and are fully embedded in the models.

Keywords: freight transportation; ports of Los Angeles and Long Beach; predictive analytics; time series analysis; moving average; exponential smoothing; trend and seasonality adjusted exponential smoothing; seasonality enhanced regression.

DOI: 10.1504/IJIOME.2024.143766

International Journal of Information and Operations Management Education, 2024 Vol.7 No.4, pp.374 - 403

Received: 18 Oct 2023
Accepted: 22 Apr 2024
Published online: 06 Jan 2025 *

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