# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 5 Conclusions  
The availability of multivariate predictive models for container throughput with a breakdown for domestic and foreign freight will be beneficial for ports, shippers, and terminal operators, as well as for national regulators and authorities. Bearing in mind the increased variability and the quick evolution of the scenario, further accelerated as various interventions are implemented to mitigate the pandemic and its fallouts, adding forecasting models based on neural networks to the arsenal of tools available has not only the potential to yield better estimates but to complete and reduce the variation when used in combination with other methods. The ability to rapidly identify trends will have effects on directing efforts towards building capacity, and this applies equally to human resources training and recruitment. Therefore, the adoption of deep learning forecasting models turns out to be useful not only for the container terminal operators (e.g., \"APM Terminals Barcelona\" and \"BEST--Barcelona Europe South Terminal\") but also for the public organisations responsible for port management at the national level (e.g., Ports of the State) and local level (e.g., Barcelona Port Authority). In fact, the demand estimation for maritime container services allows an efficient management of ports from the perspective of global competitiveness. Moreover, a correct estimate of these data allows one to strategically sustain the stable development of vertical alliances between container terminal operators and shipping companies.  
This paper sheds some light on the potential of predictive models based on neural networks to forecast the volume of containerised freight. The two recurrent neural network models outperformed SARIMA and the CNN in both of the forecasting scenarios analysed, achieving an absolute reduction in the MAPE--for the aggregate data--of about 3 percentage points in the first scenario and 1.5 percentage points in the second. The embedding realised by a neural network showed a clear spatial separation between the warmer and cooler months. The proposed approach can be extended, keeping in mind external regressors so that additional available information can be integrated. Future work will also include a refinement of the embedding used to build a meaningful representation of seasonality so that additional insights can be gained, as well as an investigation of hierarchical models.  
Conceptualisation, all authors; methodology, U.F., F.P. and S.S.; validation, all authors; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, U.F., M.R. and S.S.; visualisation, U.F. and S.S.; supervision, M.F. and F.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AIC & Akaike information criterion \\ ANN & Artificial neural network \\ ARIMA & Autoregressive integrated moving average \\ CNN & Convolutional neural network \\ GRU & Gated recurrent unit \\ LSTM & Long short-term memory \\ RNN & Recurrent neural network \\ TEU & Twenty-foot equivalent unit \\ \end{tabular}