## 2 Literature Review
RNN has good processing ability for sequential structure data and is often used in language processing problem. Gated recurrent unit (GRU) and long short-term memory (LSTM) are two representative RNN architectures. For instance, [PERSON] et al. proposed a GRU based model to forecast the estimated time of arrival for vessels. Their experimental results show that the GRU-based model can produce the best forecasting accuracy compared to other methods [34]. Moreover, [PERSON] and [PERSON] employed Adam-optimised GRU (Adam-GRU) to forecast port throughput. Their findings can be concluded as Adam-GRU can produce relatively accurate forecasting results [35]. [PERSON] et al. built a container throughput forecasting model by using LSTM. Their experiment showed that LSTM can also generate accurate forecasting results [36]. CNN is another commonly used deep learning architecture. It was originally used to solve computer vision problems, such as image recognition, and later some scholars applied CNN to the analysis and forecasting of sequence data. For instance, [PERSON] et al. proposed a temporal CNN to estimate probability density of time series [37]. There are many studies that employed CNN to build time series forecasting model [38; 39; 40; 41]. More recently, Transformer, another deep learning architecture, was first proposed by Google Brain in 2017 to solve the sequential data problem, such as natural language processing (NLP) [42]. It features all input data into the model at once, and uses positional encodings, attention, and self-attention mechanisms to capture the patterns from the data. Based on Transformer, scholars also put forward powerful NLP models such as GPT-3 [43], BERT [44], T5 [45], etc. Later, some scholars applied Transformer to time series forecasting, because time series data and text data are both sequential data [46]. Experimental results show that Transformer can produce more accurate results in time series forecasting than previous work. There have been a number of recent studies using Transformer for forecasting. All these studies suggest that Transformer has a good performance in time series forecasting [46; 47; 48; 49]. However, these studies only assessed some of these methods' performance, but no research has investigated the performance of these methods on the same time series simultaneously. Thus, which method performs better on the same time series for container throughput remains unclear. In this context, the aim of this study is to compare several existing forecasting methods for the container throughput in the same port. Then, insights for selecting an appropriate method can be suggested. \begin{table}
\begin{tabular}{c c c c} \hline
**Literature** & **Methods** & **Data** & **Main Finding** \\ \hline
[18] & ARIMA, ANN & \
\begin{tabular}{c} [PERSON]’s sunspot data, the Canadian lynx \\ data, and the British pound = US dollar \\ exchange rate data \\ \end{tabular} &
\begin{tabular}{c} The combined model can be an effective way to improve \\ forecasting accuracy achieved by either of the models \\ used separately. \\ \end{tabular} \\ \hline
[19] & ARIMA & \
\begin{tabular}{c} Spanish electricity market, Californian \\ electricity market \\ \end{tabular} &
\begin{tabular}{c} The Spanish model needs 5 h to predict future prices, as \\ opposed to the 2 h needed by the Californian model. \\ \end{tabular} \\ \hline
[21] & SARIM, SVR & Aviation factors of China & The SARIMA-SVR can provide the best forecasting results. \\ \end{tabular}
\end{table} 
Table 1: The summary of the literature.