%0 Journal Article 
%A Moscoso López, José Antonio
%A Urda, Daniel
%A Ruiz Águilar, Juan Jesús
%A González Enrique, Francisco Javier
%A Turias Domínguez, Ignacio José
%T A machine learning-based forecasting system of perishable cargo flow in maritime transport
%D 2021
%@ 1872-8286
%U http://hdl.handle.net/10498/35214
%X 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.
%K Time series forecasting
%K Machine learning
%K Ensembles
%K Perishable maritime cargo
%K Decision support systems
%~ Universidad de Cádiz