References
- 김두환.이강배(2020), LSTM 을 활용한 부산항 컨테이너 물동량 예측, 한국항만경제학회지, 제 36집 제2호, 53-62. https://doi.org/10.38121/kpea.2020.06.36.2.53
- 김창범(2015), 개입 승법계절 ARIMA와 인공신경망모형을 이용한 해상운송 물동량의 예측, 한국항만경제학회지, 제31집 제1호, 69-84.
- 김종길.박지영.왕영.박성일.여기태(2011), Study on forecasting container volume of port using SD and ARIMA(2011), 한국항해항만학회지 제35집 제4호, 343-349. https://doi.org/10.5394/KINPR.2011.35.4.343
- 민경창.하헌구(2014), SARIMA 모형을 이용한 우리나라 항만 컨테이너 물동량 예측, 대한교통학회지, 제32집 제6호, 600-614. https://doi.org/10.7470/jkst.2014.32.6.600
- 손용정.김현덕(2012), 의사결정나무분석을 이용한 컨테이너 수출입 물동량 예측, 한국항만경제학회지, 제28집 제4호, 193-207.
- 이충배.노진호(2018), 우리나라와 동아시아 항만간의 수출 컨테이너 물동량 추이 분석, 한국항만경제학회지, 제34집 제2호, 97-113.
- 여기태.정현재(2011), SD 기법에 의한 한.중.일 환적 물동량 변화량 추정에 관한 연구, 한국항만경제학회지, 제27집, 제4호, 165-185.
- 하준수.나준호.조광휘.하헌구(2021), 시계열 분석 기반 신뢰구간 추정을 활용한 항만 물동량 이상감지 방안. 한국항만경제학회지, 제37집, 제1호, 179-196.
- Chan, H. K., Xu, S., and Qi, X. (2019), A comparison of time series methods for forecasting container throughput, International Journal of Logistics Research and Applications, 22(3), 294-303. https://doi.org/10.1080/13675567.2018.1525342
- Chen, S., Goo, Y. J. J., & Shen, Z. D. (2014). A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements. The Scientific World Journal, 2014.
- Chen, S. H., & Chen, J. N. (2010). Forecasting container throughputs at ports using genetic programming. Expert Systems with Applications, 37(3), 2054-2058. https://doi.org/10.1016/j.eswa.2009.06.054
- Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140-1154. https://doi.org/10.1016/j.ejor.2006.12.004
- Diaz, R., Talley, W., and Tulpule, M.(2011), Forecasting empty container volumes, The Asian Journal of Shipping and Logistics, 27(2), 217-236. https://doi.org/10.1016/S2092-5212(11)80010-6
- Farhan, J., and Ong, G. P.(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
- Liu, C., Hu, Z., Li, Y., & Liu, S. (2017). Forecasting copper prices by decision tree learning. Resources Policy, 52, 427-434. https://doi.org/10.1016/j.resourpol.2017.05.007
- Patcha, A., & Park, J. M(2007), An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer networks, 51(12), 3448-3470. https://doi.org/10.1016/j.comnet.2007.02.001
- Patcha, A., & Park, J. M(2007), An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer networks, 51(12), 3448-3470. https://doi.org/10.1016/j.comnet.2007.02.001
- Rashed, Y., Meersman, H., Van de Voorde, E., and Vanelslander, T.(2017), Short-term forecast of container throughout: an ARIMA-intervention model for the port of Antwerp, Maritime Economics & Logistics, 19(4), 749-764. https://doi.org/10.1057/mel.2016.8
- Rahmawati, D., & Sarno, R.(2019), Anomaly detection using control flow pattern and fuzzy regression in port container handling, Journal of King Saud University-Computer and Information Sciences.
- Schulze, P. M., and Prinz, A.(2009), Forecasting container transshipment in Germany, Applied Economics, 41(22), 2809-2815. https://doi.org/10.1080/00036840802260932
- Viglioni, G., Cury, M. V. Q., & da Silva, P. A. L. (2007). Methodology for railway demand forecasting using data mining. In SAS global forum (Vol. 161, No. 2007, pp. 1-8).