Korea Trade Review (무역학회지)

Korea Trade Research Association (한국무역학회)
권호 표지
DOI QR코드

DOI QR Code

Forecasting the East Sea Rim Container Volume by SARIMA Time Series Model

SARIMA 시계열 모형을 이용한 환동해 물동량 예측

  • Min-Ju Song (Department of International Economics and Business, Yeungnam University) ;
  • Hee-Yong Lee (Department of International Economics and Business, Yeungnam University)
  • Received : 2020.09.20
  • Accepted : 2020.10.29
  • Published : 2020.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

Keywords

Acknowledgement

This work was supported by the 2019 Yeungnam University Research Grant

References

  1. Box, George EP, and George C. Tiao (1975), "Intervention analysis with applications to economic and environmental problems", Journal of the American Statistical association, 70(349), 70-79. https://doi.org/10.1080/01621459.1975.10480264
  2. Dragan, D. and T. Kramberger (2014), "Forecasting the Container Throughput in the Port of Koper using Time Series ARIMA model", Proceedings of the conference ICLST, 14.
  3. Farhan, J. and G. P. Ong (2018), "Forecasting seasonal container throughput at international ports using SARIMA models", Maritime Economics&Logistics, 20(1), 131-148. https://doi.org/10.1057/mel.2016.13
  4. G. De Jong, H. Gunn and W. Walker, (2004), "National and international freight transport models: An overview and ideas for future development", Transport Reviews, 24(1), 103-124. https://doi.org/10.1080/0144164032000080494
  5. Go. Yong-Gi, et al (2008), "Forecasting Export Loaded Container Throughput of Incheon Port", Journal of Korea Port Economic Association 24(3), 57-77.
  6. Jeong, Su-hyun, and Chang-Hoon Shin (2016), "A Study on the Impact of the Financial Crises on Container Throughput of Busan Port", Journal of Korea Port Economic Associatin, 32(2), 25-37.
  7. Jo, Jun-Ho, Je-Seop Byon and Hee-Cheul Kim (2017), "Analysis of Global Shipping Market Status and Forecasting the Container Freight Volume of Busan New port using Time-series Mode", jkiiect, 10, 295-303.
  8. Kim, Chang-Beom (2015), "Forecasting the Seaborne Trade Volume using Intervention Multiplicative Seasonal ARIMA and Artificial Neural Network Model", Journal of Korea Port Economic Association, 31(1), 69-84
  9. Lewis, Colin David (1982), Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann
  10. Min, Kyung-Chang and Hun-Koo Ha (2014). "Forecasting the Korea's Port Container Volumes With SARIMA Model", Journal of Korean Society of Transportation 32(6), 600-614. https://doi.org/10.7470/jkst.2014.32.6.600
  11. Suh, Bo-Hyoun, Tae-Woong YANG and Hun-Koo HA (2017), "Application of SARIMA Model in Air Cargo Demand Forecasting: Focussing on Incheon-North America Routes", Journal of Korean Society of Transportation 35(2), 143-159.
  12. Shin, Chang-Hoon and Su-Hyun Jeong (2011), "A study on application of ARIMA and neural networks for time series forecasting of port traffic", Journal of Navigation and Port Research, 35(1), 83-91. https://doi.org/10.5394/KINPR.2011.35.1.83
  13. Silva, Emmanuel Sirimal, and Hossein Hassani (2015), "On the use of singular spectrum analysis for forecasting US trade before, during and after the 2008 recession", International Economics 141, 34-49. https://doi.org/10.1016/j.inteco.2014.11.003
  14. Wei, Yu and Mu-Chen Chen (2012), "Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks", Transportation Research Part C: Emerging Technologies,  21(1), 148-162. https://doi.org/10.1016/j.trc.2011.06.009
  15. Yi, Ghae-Deug (2013), "Forecasting the Container Throughput of the Busan Port using a Seasonal Multiplicative ARIMA Model", Journal of Korea Port Economic Association, 29(3), 1-23.
  16. Yoo, Sang-Lok, et al (2014), "A prediction of marine traffic volume using artificial neural network and time series analysis", Journal of the Korean Society of Marine Environment &Safety, 20(1), 33-41. https://doi.org/10.7837/kosomes.2014.20.1.033
  17. Yoon, Han-Young, and Sung-Sik Park (2019), "Analysis and Estimation of Food and Beverage Sales at Incheon Int'l Airport by ARIMA-Intervention Time Series Model", Journal of the korea Academia-Industrial cooperation Society, 20(2), 458-468. https://doi.org/10.5762/KAIS.2019.20.2.458