• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3381-3388
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• 2012

This research is to find out the method appropriate for the forecasting of passennger demand using seasonal ARIMA model and efficient operation in Jeju National Airport. Time series monthly data for the investigation were collected ranging from January 2003 to December 2011. A total of 108 observations were used for data analysis. Research findings showed that the multiplicative seasonal ARIMA(0.1.2)(0.1.1)12 model is appropriate model. The number of passengers in Jeju National Airport will continue to rise, it was expected to surpass 20 million people.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.225-236
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• 2000

To compile seasonally-adjusted statistics for Korean economic statistics accurately. it is necessary to develop a Korean seasonal adjustment program. In this paper. the Korean seasonal adjustment program BOK-X-12-ARIMA, developed through modification of the US. Bureau of the Census's X-12-ARIT\IA, is explained in detail.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.205-220
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• 2016

Seasonal adjustment is useful to provide a better understanding of underlying trends in Korean economic statistics. The seasonal component also includes calendar effects such as Seol and Chuseok. Most popular seasonal adjustment methods are X-12-ARIMA of the U.S. Bureau of the Census and TRAMO-SEATS of the Bank of Spain. Statistics Korea and the Bank of Korea compile seasonally adjusted series of several Korean economic statistics. This paper illustrates basic principles for seasonal adjustment and the current status of seasonal adjustment in Korea based on previous research. In addition, several issues on seasonal adjustment are addressed.

• 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 Korean Society of Forest Science
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• v.100 no.1
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• pp.124-130
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• 2011

This study was conducted to find out appropriate model and forecast visit demand of korea national parks using seasonal ARIMA model. Data of monthly visitors uses of 18 korea national parks from January, 2003 to December, 2010 was used to analyze. The result showed that $ARIMA(1,0,0)(1,1,0)_{12}$ model was selected as a appropriate model to forecast visit demand of korea national parks and the result of post evaluation used by index of mean absolute percentage error was accurate. Therefore, the result of this study will enhance reliability and validity of forecasting technique and contribute to management strategy of korea national park.

• 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.29 no.1
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• pp.133-144
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• 2016

This paper proposes a parsimonious periodic autoregressive (PAR) model. The proposed model performance is evaluated through an analysis of Korean unemployment rate series that is compared with existing models. We exploit some common features among each seasonality and confirm it by LR test for the parsimonious PAR model in order to impose a parsimonious structure on the PAR model. We observe that the PAR model tends to be superior to existing seasonal time series models in mid- and long-term forecasts. The proposed parsimonious model significantly improves forecasting performance.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.143-159
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• 2017

For forecasting air cargo demand from Incheon National Airport to all of airports in the United States (US), this study employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the time-series data collected from the first quarter of 2003 to the second quarter of 2016. By comparing the SARIMA method against the ARIMA method, it was found that the SARIMA method performs well, relatively with time series data highlighting seasonal periodic characteristics. While existing previous research was generally focused on the air passenger and the air cargo as a whole rather than specific air routes, this study emphasized on a specific air cargo demand to the US route. The meaningful findings would support the future research.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.3-18
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• 2013

This study develops a forecasting method to estimate air cargo demand from ICN(Incheon International Airport) to all airports in EU with Seasonal Autoregressive Integrated Moving Average (SARIMA) Model using volumes from the first quarter of 2000 to the fourth quarter of 2009. This paper shows the superiority of SARIMA Model by comparing the forecasting accuracy of SARIMA with that of other ARIMA (Autoregressive Integrated Moving Average) models. Given that very few papers and researches focuses on air route, this paper will be helpful to researchers concerned with air cargo.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.600-614
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• 2014

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.