Hybrid approaches of support vector regression and SARIMA models to forecast the inspections volume

Abstract

The constant growth of air and maritime traffic of goods creates the need of increasing the number, the reliability and the security of inspections at the European borders. In this context of high security, this work applies a two-step procedure based on the hybridization of SARIMA and Support Vector Regression to forecast the inspection volume at the Border Inspection Post of Port of Algeciras Bay. Three hybrid approaches are proposed and two prediction horizons are evaluated. Based on several performance indexes to assess the goodness-of-fit of the models, the hybrid approaches perform better than the SARIMA and SVR models used separately. Hence, the study shows that the hybrid methodology improves the single methods. The experimental results can provide relevant information for resource planning and may become a decision-making tool in the inspection process of other European BIPs. © 2014 Springer International Publishing.