# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 2 Related Work  
Forecasting the container transport volume is a hot topic in the maritime transportation literature, and several approaches have been developed to improve the predictions. In this section, a brief review of the literature is conducted. Given the time-dependent nature of the phenomenon, models typical of time series analysis have been applied. The autoregressive integrated moving average (ARIMA) [27] models represent the most common approach, and some interesting empirical studies that apply these models to container transport volume forecasting can be found in the work by [PERSON] et al. [28]. Other authors have highlighted the presence of seasonal fluctuations in the container transport volume data [29; 30] and suggested the use of models able to take these effects into account. In this direction, the Seasonal-ARIMA (SARIMA) models appeared to be the natural extension of the traditional ARIMA models. [PERSON] and [PERSON] [31] applied SARIMA models to predict the container transport volume in German ports. The main limitation of the ARIMA and SARIMA models is the assumption of linearity, which could make them unable to capture the complexity present in some empirical evidence. The need for more accurate predictions directed scientific interest towards more complex and non-linear models. Some interesting examples are trigonometric regression models [30] and machine learning-based models, such as artificial neural networks (ANNs) [32] and support vector machines (SVMs) [33]. Focusing on an intercontinental liner service, [PERSON] and [PERSON] [34] proposed a method for forecasting the number of containers to be transported between two continents. They used piecewise linear regression, autoregression, and artificial neural networks. Using real-case booking data, they found that a combination of the three methods yielded forecasts of satisfactory precision. To cope with changing trends and limited data, [PERSON] and [PERSON] [35] developed a predictive method based on a GM(1,1) grey model and generalised contour lines to forecast Chinese port cargo throughput. Fouries analysis was used in combination with a SARIMA model in an empirical study of cargo and container throughput for the ports of Hong Kong and Kaohsiung. Numerous other approaches, based on decomposition methods or hybrid models, have been proposed. The interested reader may refer to the classic book by [PERSON] et al. [27]. The mainstream literature, especially in the last period, has shown a growing interest in machine learning (ML). [PERSON] et al. [36] provided an exhaustive review of the progress in ML for transportation management. The interesting point of forecasting with ANNs is that the optimal model is determined from the data itself. The first paper that employed ANNs in this field is dated 1999, when an ANN-based model was used to predict the volume of container trade in the port of Kaohsiung in Taiwan [37]. More recent studies are proposed by [PERSON] et al. [38], who analysed data relating to the port of Bangkok, and [PERSON] et al. [39], who focussed on data from the port of Barcelona. Working on data relating to the port of Shanghai and Shenzhen, [PERSON] et al. [40] proposed a hybrid approach to predicting container trading volume based on the least squares SVR (a regression version of the traditional least squares SVM method) and offered an interesting comparative analysis with some models, including ARIMA and SARIMA models. Notable new approaches include the one by [PERSON] et al. [41], who discussed a generative model based on an autoencoder coupled with an ANN for forecasting the quality of production, the one by [PERSON] et al. [42], who incorporated dynamic factors into a recurrent network, and the one by [PERSON] et al. [43], who combined variational mode decomposition to isolate multiple seasonal components and an extreme learning machine. Another relatively recent paper proposes a univariate long short-term memory (LSTM) network model to forecast the future volume of container transport [44].