Container Demand Forecasting at Border Posts of Ports: A Hybrid SARIMA-SOM-SVR Approach
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An accurate forecast of freight demand at sanitary facilities of ports is one of the key challeng-es for transport policymakers to better allocate resources and to improve planning operations. This paper proposes a combined hybrid approach to predict the short-term volume of containers passing through the sanitary facilities of a maritime port. The proposed methodology is based on a three-stage process. First, the time series is decomposed into similar smaller regions easier to predict using a self-organizing map (SOM) clustering. Then, a seasonal auto-regressive integrated moving averages (SARIMA) model is fitted to each cluster, obtaining predicted values and residuals of each cluster. A support vector regression (SVR) model is finally applied in each cluster using the historical data clustered and the predicted variables from the SARIMA step, testing different hybrid configurations. The experimental results demonstrated that the proposed model outperforms other methodologies based on SVR. The proposed model can be used as an automatic decision-making tool by seaport or airport management due to its capacity to plan resources in advance.
KeywordsContainer forecasting Machine learning Support vector regression Self-organizing maps Hybrid models
Authors acknowledge support through grant RTI2018-098160-B-I00 from MINECO-SPAIN. The database has been kindly provided by the Port Authority of Algeciras Bay.
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