• The Korean Journal of Applied Statistics
• /
• v.22 no.5
• /
• pp.1007-1017
• /
• 2009

Forecasting for air demand such as passengers and freight has been one of the main interests for air industries. This research has mainly focus on the comparison the performance between the univariate seasonal ARIMA models and the multivariate time series models. In this paper, we used real data to predict demand on international passenger and freight. And multivariate time series models are better than the univariate models based on the accuracy criteria.

• The Korean Journal of Applied Statistics
• /
• v.36 no.5
• /
• pp.457-471
• /
• 2023

The monthly series is an aggregation of daily values. In the absence of observable daily data, calendar effects such as trading day and holidays are estimated using a RegARIMA model. However, if the daily series were observable, these calendar effects could be estimated directly from the daily series, potentially improving the seasonal adjustment of the monthly time series. In this paper, we propose a method to improve the seasonal adjustment of monthly time series by using calendar variation estimation based on daily time series. We apply this seasonal adjustment method to three monthly time series and compare our results with those obtained using X-13ARIMA-SEATS.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.93-102
• /
• 2011

In this paper, we compared the performance of the several time series models for tourism demand forecasting. We showed that seasonal effects in the data(Japan, China, USA, and Philippines) exist in the tourism data and the forecasting accuracies are compared by the RMSE criterion.

• The Korean Journal of Applied Statistics
• /
• v.34 no.4
• /
• pp.623-632
• /
• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

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

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

• Proceedings of the Korea Water Resources Association Conference
• /
• 2010.05a
• /
• pp.1395-1399
• /
• 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
• /
• v.32 no.1
• /
• pp.139-148
• /
• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.133-144
• /
• 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 the Korea Academia-Industrial cooperation Society
• /
• v.13 no.8
• /
• pp.3381-3388
• /
• 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.