대한교통학회지 (Journal of Korean Society of Transportation)

  • 제32권6호
  • /
  • Pages.600-614
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  • 2014
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  • 1229-1366(pISSN)
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  • 2234-4217(eISSN)
대한교통학회 (Korean Society of Transportation)
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SARIMA 모형을 이용한 우리나라 항만 컨테이너 물동량 예측

Forecasting the Korea's Port Container Volumes With SARIMA Model

  • 민경창 (한국해양수산개발원 국제물류연구실) ;
  • 하헌구 (인하대학교 물류전문대학원)
  • Min, Kyung-Chang (International Logistics Research Department, Korea Maritime Institute) ;
  • Ha, Hun-Koo (Graduate School of Logistics, Inha University)
  • 투고 : 2014.05.08
  • 심사 : 2014.10.03
  • 발행 : 2014.12.31
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초록

본 연구는 SARIMA 모형을 활용하여 기존에 다루어지지 않았던 분기별 항만 컨테이너 물동량을 예측하였다. 구체적으로 모델 추정에 활용된 자료는 1994년 1사분기부터 2010년 4사분기까지 총 84분기동안의 국내 전체 항만 컨테이너 물동량 자료이다. 본 연구에서 추정된 예측 모형의 예측 정확도를 검증하기 위하여 2011년 1사분기부터 2013년 4사분기까지 물동량을 예측하여 실제 물동량과 비교하였다. 또한 기존에 널리 활용되고 있는 ARIMA 모형을 활용하여 추정한 예측 모형과의 비교를 통해 분기별 항만 물동량 예측에 있어서 SARIMA 모형의 상대적 우수성을 검증하였다. 기존에 항만 물동량을 예측하는 대부분의 연구는 주로 장기 예측에 초점이 맞추어져 있다. 또한 월별, 연도별 물동량 자료가 활용된 경우가 대부분이다. 분기별 항만 컨테이너 물동량 자료를 활용하여 단기 수요를 예측함과 동시에 SARIMA 모형의 우수성을 입증한 본 연구는 충분한 가치가 있다고 판단된다.

This paper develops a model to forecast container volumes of all Korean seaports using a Seasonal ARIMA (Autoregressive Integrated Moving Average) technique with the quarterly data from the year of 1994 to 2010. In order to verify forecasting accuracy of the SARIMA model, this paper compares the predicted volumes resulted from the SARIMA model with the actual volumes. Also, the forecasted volumes of the SARIMA model is compared to those of an ARIMA model to demonstrate the superiority as a forecasting model. The results showed the SARIMA Model has a high level of forecasting accuracy and is superior to the ARIMA model in terms of estimation accuracy. Most of the previous research regarding the container-volume forecasting of seaports have been focussed on long-term forecasting with mainly monthly and yearly volume data. Therefore, this paper suggests a new methodology that forecasts shot-term demand with quarterly container volumes and demonstrates the superiority of the SARIMA model as a forecasting methodology.

키워드

참고문헌

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피인용 문헌

  1. EMU(Empty Management Unit)의 운영현황 및 개선방안에 관한 연구 vol.15, pp.12, 2014, https://doi.org/10.14400/jdc.2017.15.12.273
  2. 시계열 분석 기반 신뢰구간 추정을 활용한 항만 물동량 이상감지 방안 vol.37, pp.1, 2021, https://doi.org/10.38121/kpea.2021.03.37.1.179