# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 2 Background
### Sequential Models with Container Volume Prediction  
Traditional time-series prediction exhibits good performance in short-term prediction; however, long-term prediction results rarely reflect unpredictable external economic situations [4]. With advances in technology and the use of various external variables in the prediction model to compensate for these limitations, there is a visible improvement in the performance; however, with this improvement, the problem of predicting the trend (up, steady, or down) of the volume is newly presented [6].  
Comparative studies were conducted between the ARIMA model, a representative nonlinear forecasting model for container volume prediction of domestic ports, and a hybrid model obtained by combining the ARIMA model and an artificial neural network (ANN) model to enhance container volume prediction [10]. The results show that ANN and hybrid models exhibit better predictive performance than conventional ARIMA models. Based on these results, it was verified that the performance of deep learning-based models was better than that of existing traditional models.  
Another study was conducted to compare deep learning and various time-series prediction models in the monthly container volume prediction of Singapore ports. It was noted that network LSTM models recorded the best performance [11]. Furthermore, we propose that multivariate LSTM neural networks using external variables perform better than the traditional time-series models.  
In [12], RNNs and LSTMs were used to study daily container volume prediction based on deep learning to improve the efficiency of yard and ship loading plans at port terminals. In the LSTM-RNN model, the prediction error was approximately 12.39%, which was lower than that in human-based prediction. Hence, this study demonstrates that the performance of daily prediction of container volume based on deep learning using historical data is better than the empirical prediction by humans. This presented the current problem of relying on empirical decision-making in the port industry and highlighted the operational efficiency augmentation based on deep learning using time-series data [12].  
The study of volume prediction at Busan port in Korea, indicated that the development of the port is closely related to national competitiveness and in fact, strengthens it; hence, accurate prediction of container volume is essential [4]. For this prediction, an LSTM model was used among the various deep learning models because trends and patterns of time-series data alone cannot represent variability due to rapid changes in the shipping and port logistics industry, such as economic market changes, financial crises, and variations in ship size. Thus, it was observed that the performance of volume prediction based on deep learning performs better than the existing SARIMA-based model.  
The application of deep learning for volume prediction has resulted in improved performance over traditional statistical models. In addition, introducing external variables that are flexible to accept variability in volume and not limited to univariate prediction in utilizing deep learning cannot be overlooked.