A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic

The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously.

show abstract

A Study on Estimating Container Throughput in Korean Ports using Time Series Data

Kim¹,

Lu²

2016

Journal of Navigation and Port Research

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Study on Application of ARIMA and Neural Networks for Time Series Forecasting of Port Traffic

Cited by 5 publications

References 14 publications

GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries

GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries

Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models

A Study on Estimating Container Throughput in Korean Ports using Time Series Data

Contact Info

Product

Resources

About