@misc{10498/35214,
year = {2021},
url = {http://hdl.handle.net/10498/35214},
abstract = {The uncertainty cargo flow problem establishes a limitation in ports management where decisionmaking
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.},
publisher = {Elsevier},
keywords = {Time series forecasting},
keywords = {Machine learning},
keywords = {Ensembles},
keywords = {Perishable maritime cargo},
keywords = {Decision support systems},
title = {A machine learning-based forecasting system of perishable cargo flow in maritime transport},
doi = {10.1016/J.NEUCOM.2019.10.121},
author = {Moscoso López, José Antonio and Urda, Daniel and Ruiz Águilar, Juan Jesús and González Enrique, Francisco Javier and Turias Domínguez, Ignacio José},
}