• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.1
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• pp.66-75
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• 2012

The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1715-1721
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• 2011

For an efficient railroad operations the demand forecasting is required. Time series models can quickly forecast the future demand with fewer data. As well as the accuracy of forecasting is excellent compared to other methods. In this study is proposed the intervention ARIMA model for forecasting methods of KTX passenger demand. The intervention ARIMA model may reflect the intervention such as the Kyongbu high-speed rail project second phase. The simple seasonal ARIMA model is predicted to overestimate the KTX passenger demand. However, intervention ARIMA model is predicted the reasonable results. The KTX passenger demands were predicted to be a week units separated by the weekday and weekend.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Journal of the Korean Society for Railway
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• v.14 no.5
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• pp.470-476
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• 2011

This study proposed the intervention ARIMA model as a way to forecast the KTX passenger demand. The second phase of the Gyeongbu high-speed rail project and the financial crisis in 2008 were analyzed in order to determine the effect of time series on the opening of a new line and economic impact. As a result, the financial crisis showed that there is no statistically significant impact, but the second phase of the Gyeongbu high-speed rail project showed that the weekday trips increased about 17,000 trips/day and the weekend trips increased about 26,000 trips/day. This study is meaningful in that the intervention explained the phenomena affecting the time series of KTX trip and analyzed the impact on intervention of time series quantitatively. The developed model can be used to forecast the outline of the overall KTX demand and to validate the KTX O/D forecasting demand.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.sup1
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• pp.85-90
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• 2016

Two years after implementation of the graphic health warning intervention in Vietnam, it is very important to evaluate the intervention's potential impact. The objective of this paper was to predict effects of graphic health warnings on cigarette packages, particularly in reducing cigarette demand and smoking-associated deaths in Vietnam. In this study, a discrete choice experiment (DCE) method was used to evaluate the potential impact of graphic tobacco health warnings on smoking demand. To predict the impact of GHWs on reducing premature deaths associated with smoking, we constructed different static models. We adapted the method developed by University of Toronto, Canada and found that GHWs had statistically significant impact on reducing cigarette demand (up to 10.1% through images of lung damage), resulting in an overall decrease of smoking prevalence in Vietnam. We also found that between 428,417- 646,098 premature deaths would be prevented as a result of the GHW intervention. The potential impact of the GHW labels on reducing premature smoking-associated deaths in Vietnam were shown to be stronger among lower socio-economic groups.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.4
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• pp.77-89
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• 2013

The purpose of this study is to anticipate the air travel demands over the period of 164 months, from January 1997 to August 2010 using ARIMA-Intervention modeling on the selected sample data. The sample data is composed of the number of the passengers who in the domestic route for Jeju route. In the analysis work of this study, the past events which are assumed to have affected the demands for the air travel routes to Jeju in different periods were used as the intervention variables. The impacts of such variables were reflected in the presupposed demand. The intervention variables used in this study are, respectively, the World Cup event in 2002 (from May to June), 2003 SARS outbreak (from April to May), Tsunami in January 2005, and the influenza outbreak from October to December 2009. The result of the above mentioned analysis revealed that the negative intervention events, like a global outbreak of an epidemic did have negative impact on the air travel demands in a risk aversion by the users of the aviation services. However, in case of the negative intervention events in limited area, where there are possible substituting destinations for the tourists, the impact was positive in terms of the air travel demands for substituting destinations due to the rational expectation of the users as they searched for other options. Also in this study, it was discovered that there is not a binding correlation between a nation wide mega-event, such as the World Cup games in 2002, and the increased air travel demands over a short-term period.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.103-108
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• 2015

The electric utility has the responsibility of reducing the impact of peaks on electricity demand and related costs. Therefore, they have introduced Direct Load Control System (DLCS) to automate the external control of shedding customer load that it controls. Since the number of customer load participating in the DLC program are keep increasing, DLCS operators a re facing difficulty in monitoring and controlling customer load. The existing DLCS needs constant operator intervention, e.g., whenever the load is about to exceed a predefined amount, it needs operator's intervention to control the on/off status of the load. Therefore, DLCS operators need the state-of-the-art DLCS, which can control automatically the on/off status of the customer load without intervention as much as possible. This paper presents an intelligent DLCS using the active database. The proposed DLCS is applying the active database to DLCS which can avoid operator's intervention as much as possible. To demonstrate the validity of the proposed system, variable production rules and intelligent demand controller are presented.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 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.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.449-458
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• 2011

Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. Recently government agency installed COD (chemical oxygen demand) auto-monitering machines in river. In this article we provide an outlier detection algorithm using R based on the time series intervention model that detects some outlier values among those COD time series values generated from an auto-monitering machine. Through this algorithm using R, we can achieve an automatic algorithm that does not need manual intervention in each step, and that can further be used in simulation study.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.725-732
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• 2016

This study analyzes the number of Jeju-bound tourists according to travellers' purposes. We classify the travellers' purposes into three categories: "Rest and Sightseeing", "Leisure and Sport", and "Conference and Business". To see an impact of MERS outbreak occurred in May 2015 on the number of tourists, we fit seasonal ARIMA-Intervention model to the monthly arrivals data from January 2005 to March 2016. The estimation results show that the number of tourists for "Leisure and Sport" and "Conference and Business" were significantly affected by MERS outbreak whereas arrivals for "Rest and Sightseeing" were little influenced. Using the fitted models, we predict the number of Jeju-bound tourists.