## 5 Conclusions  
This research is a comparison study of nine forecasting methods on container throughput time series, four of which are traditional regression-based methods, and five of which are machine learning-based methods. The main finding of this study is that GRU is a method that can produce more accurate results with higher probability when constructing container throughput forecasting models. Another finding is that NM can be used for rapid and simple container throughput estimation when computing equipment and services are not available. The study also confirmed that machine learning methods are still the better choice over some traditional methods. An important conclusion that can be drawn from  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Port** & **NM** & **MA** & **AR** & **ARIMA** & **MLP** & **GRU** & **LSTM** & **CNN** & **Transformer** \\ \hline Antwerp & 60.71 & 84.19 & 158.45 & 39.46 & 489.99 & **21.43** & 56.63 & 51.65 & 71.64 \\ Busan & 71.54 & 150.09 & 53.32  
the analysis of experimental results is that machine learning methods are useful for training forecasting models, but the characteristics of the data can affect the performance of the methods. Therefore, machine learning methods are not necessarily better than traditional forecasting methods. In other words, one should be cautious about using machine learning methods to build forecasting models. This study compares the performance of different methods on multiple time series, and these time series are characterised by short observation period and small number of observations. Therefore, the conclusion of this study is applicable to any time series with the same time characteristics as this study.  
Although this study explores the performance of nine different methods in forecasting the throughput of the world's top 20 container ports, there are still limitations. As the hub of world trade, the change of port throughput is not only determined by the port city, but also determined by the operation situation of various ports around the world and the development of world trade market. This study only uses historical port throughput data as the data source. Therefore, the future research direction is to add the above influencing factors such as the development of port facilities, economic data of port cities, transportation between the port and other ports into the forecasting model and analyse their impact on port container throughput.  
Conceptualization, S.X. and F.Z.; methodology, S.X.; software, S.X. and S.Z.; validation, S.Z., J.H., and W.Y.; formal analysis, S.X.; investigation, S.X.; resources, S.X. and F.Z.; data curation, S.X.; writing original draft preparation, S.X.; writing review and editing, F.Z.; visualization, S.Z., J.H., and W.Y.; supervision, S.X. and F.Z; project administration, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analysed in this study. This data can be found here: [[https://github.com/tdjuly/Port-container-throughput](https://github.com/tdjuly/Port-container-throughput)]([https://github.com/tdjuly/Port-container-throughput](https://github.com/tdjuly/Port-container-throughput)) (accessed on 20 September 2022).  
Not applicable.  
The authors declare no conflict of interest.