• Journal of Korea Port Economic Association
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• v.29 no.3
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• pp.1-23
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• 2013

This paper estimates and forecasts the container throughput of Busan port using the monthly data for years 1992-2011. To do this, this paper uses the several seasonal multiplicative ARIMA models. Among several ARIMA models, the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$ is selected as the best model by AIC, SC and Hannan-Quin information criteria. According to the forecasting values of the selected seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$, the container throughput of Busan port for 2013-2020 will increase steadily annually, but there will be some volatile variations monthly due to the seasonality and other factors. Thus, to forecast the future container throughput of Busan port and to develop the Busan port efficiently, we need to use and analyze the seasonal multiplicative ARIMA model $(1,0,1){\times}(1,0,1)_{12}$.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.149-162
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• 2007

The purpose of this study is to forecast the marine trading volumes using multiplicative seasonal Autoregressive Integrated Moving Average(ARIMA) model. The paper proceeds by comparing the forecasting performances of the unload volumes with those of the load volumes with Box-Jenkins ARIMA model. Also, I present the predicted values based on the ARIMA model. The result shows that the trading volumes increase very slowly.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.133-135
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• 2013

장래의 해상교통량에 대한 정확한 예측은 항로설계 및 해상교통의 안전성 평가 측면에서 중요한 요소이다. 본 연구는 신뢰성 있는 해상교통량을 추정하기 위해 시계열 모델의 지수평활법과 ARIMA 모형을 이용하여 모형의 식별 및 진단 방안을 제시하였다. 제시된 방법의 효과를 검증하기 위하여 주요항만인 부산항, 광양항, 인천항, 평택항의 해상교통량을 예측하였다. 그 결과로 부산항은 ARIMA 모형, 광양항은 Winters 승법 모형, 인천항은 단순계절 모형, 평택항은 ARIMA 모형이 더 적합한 모형으로 알 수 있었으며, 각 항만별 계절에 따라 월별 교통량의 차이를 보이는 것으로 분석되었다. 본 연구 결과는 향후 항로 및 항만설계 또는 해상교통 안전성 평가에 보다 신뢰성 있는 추정치를 제공할 수 있을 것으로 보인다.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.159-167
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• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1395-1399
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• 2010

최근 들어 전 세계적으로 태풍과 가뭄 그리고 국지적인 호우 등의 기상변화로 인하여 수자원 종합적인 개발과 이용계획에 대한 전문적인 예측이 필요하다. 우리나라는 홍수기에 집중적인 강우 발생으로 인하여 평수기와 유입량 차이가 심한 수문특성을 가지고 있어 안정적인 수자원 공급에 대한 장기적인 관점에서 이수와 치수정책을 수립해야 한다. 본 연구는 1985년 1월부터 2008년 12월까지 24년에 해당하는 한정된 기간의 짧은 유출량 자료를 갖는 대청댐 유역에서의 시계열 유입량 특성을 Box-Jenkins모형 또는 ARIMA모형을 적용하여 추계학적 분석을 실시하였다. 월유입량과 같은 비정상성 시계열에 적용될 수 있는 적절한 추계학적 모형을 찾기 위하여 모형의 식별과 모형의 추정, 모형의 검진 등의 3단계에 걸친 분석을 실시하였다. 연구결과 대청댐 월유입량 예측모형으로 승법계절 ARIMA$(0,1,2){\times}(1,1,0)_{12}$이 유도되었으며, 이 모형으로 1, 3, 6, 12개월의 선행기간에 대한 실시간 유입량을 예측하였다. 예측된 유입량을 2008년 실측유입량과 비교한 결과 6개월에 대한 예측의 정확성이 가장 높게 나타났다. 또한 평수기와 홍수기를 구분한 예측도 실시하였으며, 평수기는 1개월 홍수기는 3개월 간격으로 예측하는 것이 가장 적절한 것으로 분석되었다.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.419-430
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• 2014

An accurate prediction of electricity supply and demand is important for daily life, industrial activities, and national management. In this paper electricity sales is predicted by time series modelling. Real data analysis shows the transfer function model with cooling and heating days as an input time series and a pulse function as an intervention variable outperforms other time series models for the root mean square error and the mean absolute percentage error.

• Journal of Korea Port Economic Association
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• v.25 no.1
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• pp.1-14
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• 2009

This study predicted Port trade volume by considering Korea's export to China and import Com China separately using ARIMA model (Multiplicative Seasonal ARIMA Model). We predicted monthly Port trade volumes for 27 months from October 2008 to December 2010 using monthly data from September 2008 to January 2001 using monthly data. As a result of prediction, we found that the export volume decreased in January, February, August and September while the import volume decreased in February, March, August and September. As the decrease period was clearly differentiated, it was possible to predict export and import volumes. Therefore, it is believed that the results of this study will generate useful basic data for policy makers or those working for export and import enterprises when they set up policies and management plans. And to improve competitive power of Port trade, this study suggests privatization of Port, improvement of information capability, improvement of competitive power of Port management companies, support for Port distribution companies, plans for active encouragement of transshipment, and management of added value creation policy.

• Journal of Agricultural Extension & Community Development
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• v.20 no.2
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• pp.511-533
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• 2013

The study used a seasonal ARIMA model to forecast the number of tourists to Kyeongju foreign in a uni-variable time series. Time series monthly data for the investigation were collected ranging from 1995 to 2010. A total of 192 observations were used for data analysis. The date showed that a big difference existed between on-season and off-season of the number of foreign tourists in Kyeongju. In the forecast multiplicative seasonal ARIMA(1,1,0) $(4,0,0)_{12}$ model was found the most appropriate model. Results show that the number of tourists was 694 thousands in 2011, 715 thousands in 2012, 725 thousands in 2013, 738 thousands in 2014, and 884 thousands in 2015. It was suggested that the grasping of the Kyeongju forecast model was very important in respect of how experts in tourism development, policy makers or planners would establish marketing strategies to allocate services in Kyeongju as a tourist destination and provide tourism facilities efficiently.

• Korean Security Journal
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• no.30
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• pp.139-169
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• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.