A machine learning-based forecasting system of perishable cargo flow in maritime transport

• Published in: Neurocomputing (Amsterdam) 2021-09, Vol.452, p.487-497
• Main Authors: Moscoso-López, José Antonio, Urda, Daniel, Ruiz-Aguilar, Juan Jesús, González-Enrique, Javier, Turias, Ignacio J.
• Format: Article
• Language: English
• Subjects: Decision support systems; Ensembles; Machine learning; Perishable maritime cargo; Time series forecasting
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.10.121

The uncertainty cargo flow problem establishes a limitation in ports management where decision-making processes need accurate information of the future values. This work aims at predicting the future values of Ro-Ro perishable cargo flow at the Port of Algeciras Bay using a machine learning-based forecasting system. Two datasets consisting of daily records of fresh fruits and vegetables between 2010 to 2017 were analyzed. Additionally, these two--time series were pre-processed applying an exponential moving average method to obtain a smoothed version of the original ones. Multiple Linear Regression, Support Vector Machines, Long Short-Term Memory networks and an ensemble approach have been used to build a forecasting system and obtain the future values of the perishable cargo. The results of the analysis showed how this machine learning-based system achieved 14.83% better performance rate than a baseline persistence model in terms of root mean squared error in the fresh fruits dataset and 11.3% better in the vegetables one. In general, models’ average performance rates are better using the smoothed version of the times series rather than the original ones.