## 1 Introduction  
Over the last decades, ports have played an important role in international trade and most of the overseas shipping of products is aboard deep-sea container vessels [1]. The increase in traffic of goods and the European unification has led to consider the enhancement of security at border crossings of the European Union. In this sense, the Border Inspection Posts (BIPs) were created in order to guarantee the security at border crossings and the quality of the import-export goods. BIPs are the approved facilities where the checks of goods (transported within containers by trucks or towing vehicles) are carried out before entering the Community territory. However, the sustained growth in the worldwide exchange of goods is creating the need for further inspections resulting in congestion and high load-peaks within the sanitary facilities. This causes time delays and higher costs in the supply chain. The BIPs are thereby bottlenecks that must be necessarily assessed by Port Authorities in order to keep the quality level of the port and avoid losing competitiveness. In order to avoid time delays and congestion in the sanitary facilities, the port management should be able to accurately forecast the number of container passing through these facilities. An accurate prediction of the volume of container traffic through the BIP may become a useful tool to improve human resources, planning operations and the service quality at ports. Forecasting time series have focused attention of many researchers for a long time. Many efforts have been carried out to improve the existing methodologies and to achieve enhanced models to obtain accurate predictions in any forecasting fields (for instance, energy consumption, transportation, environment, economy, medicine and health). The focus of this study is mainly set on maritime transport due to the traffic associated with the port BIPs. The proposed forecasting techniques can be divided into three categories: single methods, combined methods and hybrid methods. The first class comprises both linear and nonlinear techniques. Linear techniques are based on the assumption that a linear relationship exists between the future values and the current and past values of the time series. One of the linear models that has attracted more attention over the past few decades is the well-known autoregressive integrated moving averages (ARIMA) model and its further extended versions. Based on the [PERSON] and Jenkins methodology [2] the ARIMA models have been constantly applied to solve forecasting tasks related to maritime transport. ARIMA models were successfully applied to predict export operations in container terminals [3], to predict certain traffic flows of goods that pass through a port [4; 5] and recently for predicting container throughput volumes at ports [6]. Moreover, nonlinear techniques have become a strength alternative against the weaknesses of linear models. This forecasting technique has proved to be effective when time series show nonlinear patterns, overcoming the main constraints of linear models. Real-world problems are complex and mostly generated by an underlying nonlinear process [7], such could be the case of volume of containers demand at BIPs. In this subcategory, two machine learning techniques highlight: artificial neural networks (ANNs) and support vector machines for regression (SVR). Instead of ANNs, SVR has attracted increasing attention in recent years due to its inherent abilities to overcome some general limitations of ANNs, such as the achievement of a global minimum. Due to its great generalization ability, SVR has been used in forecasting transport tasks with promising results. These two techniques have been constantly compared in the research literature. The second category comprises the combined models. These models attempt to overcome the constraints of the single forecasting models when time series exhibit seasonal features, temporal evolutions, correlations, and other patterns hard to capture by single models.