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A machine learning-based forecasting system of perishable cargo flow in maritime transport.
- Source :
-
Neurocomputing . Sep2021, Vol. 452, p487-497. 11p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 452
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 150770464
- Full Text :
- https://doi.org/10.1016/j.neucom.2019.10.121