## 1 Introduction
To this aim, single methods are combined to seize the abilities of each one in certain forecasting tasks. One of the most frequently used approaches consists of combining a single prediction technique (mainly a soft computing technique) with a clustering method. Based on the divide-and-conquer principle, clustering methods allow to divide the original database into a number of smaller groups, called clusters. The main assumption is that simpler clusters are easier to solve than addressing the whole database. When the clustering method has divided the database into several clusters, a prediction technique is then applied in each cluster independently. Self-organizing maps (SOMs) [8] is probably the best-known clustering method. This popular unsupervised learning technique has been proved to be faster and more accurate and efficient than other clustering methods [9], improving the final prediction performance in time series [10]. A combined SOM-ANN model was introduced by [PERSON] et al. [11] to predict traffic flows in transportation. This work also compared the forecasting performance of the proposed model to the obtained with a SOM-ARIMA model and a single ARIMA model. Results showed that the SOM-ANN model outperformed the rest of models. Due to the recent emergence of SVR in transportation, there is hardly any research related to transport combining SOM and SVR in a two-stage procedure. Nevertheless, it is a widespread solution in many other forecasting fields [12]. The third category includes hybrid models. Single linear models have shown to have several constraints, limitations and disadvantages in particular situations and certain applications (for instance, when the underlying generating mechanism is nonlinear or uncertainty is presented) [13]. In a similar manner, the use of nonlinear models can be totally inappropriate if linear patterns are presented. Moreover, real-world time series are not completely linear or nonlinear, but rather contain both components. Thus, a methodology using linear and nonlinear models in a hybrid way takes the capabilities of both models, improving the forecasting accuracy and reducing the risk of failure when an unsuitable single model is used. Hybridizing linear models and machine learning techniques have been proposed in recent years to forecast transportation time series. Particularly, ARIMA has been the most commonly used linear model in literature to construct this kind of hybrid model. The first works were proposed considering ANNs as the machine learning technique in [7; 14] to forecast time series and they concluded that hybrid ARIMA-ANN models were superior to single SARIMA and ANN models. Since then, many research works can be found in the literature in many areas linked to time series analysis [15; 16]. Several authors were also proposed a hybridization of SARIMA and SVR to address several forecasting tasks outside the transport sector [17; 18], although it is less widespread compared to SARIMA-ANN models. Related to maritime transport, [PERSON] et al. [19] proposed several hybrid approaches in a comparative way including the SARIMA-SVR model for container throughput forecasting. All these previous studies coincided in pointing out that a hybrid strategy considering ARIMA and SVR models overcame the performance of single models in their respective domains. In this study, a combined-hybrid forecasting model is proposed in such a way that a hybrid model (SARIMA-SVR) is combined with a clustering method (SOM) to forecast the daily number of containers passing through a BIP, thus resulting in a SOM-SARIMA-SVR model. This methodology unifies the strengths of clustering methods in decomposing the forecasting task into some relatively easier subtasks (using a SOM method) and the strengths of hybrid models to fit linear and nonlinear components (using a SARIMA-SVR model).