• 대한산업공학회지
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• 제25권1호
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• 경영과학
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• 제21권3호
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• pp.129-147
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• 2004

Scenario analysis for strategic planning, unlike most forecasting methods, provides a qualitative, contextual description of how the present will evolve into the future. It normally tries to identify a set of possible futures, each of whose occurrence is plausible but not assured. In this paper, we propose the use of Cross Impact Analysis(CIA) approach for scenario generation about the future of Korean IT environments. In this analysis, we classified IT environments into technical, social, legislative, and economic factor. And various variables and events were defined in each factor. From the survey collected from IT related experts, we acquire probability of occurrence and compatibility estimates of every possible pairs of events as input. Then 2 phase analysis is used in order to choice events with high probability of occurrence and generate scenario. Finally, after CIA using Monte Carlo simulation, a detail scenario for 2010 was developed. These scenario drawn from the CIA approach is a result considered by cross impacts of various events.

• 경영과학
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• 제33권3호
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.165-168
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• 2006

Exponential smoothing methods do not adapt well to unexpected changes in underlying process. Over the past few decades a number of adaptive smoothing models have been proposed which allow for the continuous adjustment of the smoothing constant value in order to provide a much earlier detection of unexpected changes. However, most of previous studies presented ad hoc procedure of adaptive forecasting without any theoretical background. In this paper, we propose a detection-adaptation procedure applied to simple and Holt's linear method. We derive level and slope change detection statistics based on Bayesian statistical theory and present distribution of the statistics by simulation method. The proposed procedure is compared with previous adaptive forecasting models using simulated data and economic time series data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.432-434
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• 2002

Recently, electricity demand of remote islands in Korea has been rapidly increased. It's mainly due to increase of income level resulted from economic development. Electricity demand patterns and characteristics in remote islands are different from those of mainland in point of time of peak load, demographic and industrial characteristics of islands, and so on. The optimal power supply in remote islands has a important relationship with accurate analysis of island's load characteristics, the adoption of relevant load forecasting technique, and optimal power facilities reflecting local's electricity demand characteristics. This paper shows the recent load pattern and characteristics, load forecasting using probability distribution, and the perpetration of relevant power facilities in remote islands.

• 대한안전경영과학회지
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• 제13권2호
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• pp.267-273
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• 2011

Since 'The Act on Private Investment of The Infrastructure' was established in 1994, private investment as well as government's investment in transport infrastructure has been active. However investment in transport infrastructure has more risks than others' due to uncertainty both in traffic volume and in construction cost. In the current appraisal procedure of deciding transportation infrastructure investment, instead of risk management, the sensitivity analysis considering only the changes of benefit, cost and social discount rate which are main factor affecting economic feasibility is carried out. Therefore the uncertainty of various factors affecting demand, cost and benefit are not considered in feasibility study. In this study the problems in current investment appraisal system were reviewed. Using Delphi technique the major factors which have high uncertainty in feasibility study were surveyed and then improvement plan was suggested in the respective of classic 4 step demand forecasting method. The range estimation technique was also mentioned to deal with the uncertainty of the future.

• 천문학회보
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• 제37권2호
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• pp.129.2-129.2
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• 2012

Solar flares and coronal mass ejections (CMEs) are two major solar eruptive phenomena which can cause enormous economic and commercial losses: (1) flares are sudden, rapid, and intense brightenings from radio waves to Gamma-rays in the chromosphere and corona, and (2) CMEs are large-scale transient eruptions of magnetized plasma from the solar corona that propagate outward into interplanetary space. Most flares and CMEs occur in magnetically complicated solar active regions (ARs). Therefore, it is crucial to investigate magnetic fields in ARs and their temporal variations for understanding a precondition and a trigger mechanism related to flare/CME initiation. In this presentation, we will introduce an automated system for empirical forecasting of flares and CMEs in ARs using full-disk photospheric line-of-sight magnetogram data taken by the Helioseismic and Magnetic Imager (HMI) onboard the SDO.

• 대한교통학회지
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• 제7권2호
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• pp.29-43
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• 1989

The purpose of this study is to construct a forecasting model involved in a diverted traffic volume of the 2nd intra-urban expressway in construction presently, in the case of the future prediction of traffic demand for the intra-urban expressway in Pusan. In this study, the model involved in a diverted traffic volume is constructed trustworthy. And the future traffic demand of intra-urban expressway by this model was forecasted 114,005 volume/daily in 1996 and 147,090 volume/daily in 2001. However, it will made a study more and more concretely for practicality and limitation as well as construction of the forecasting model considered an intrinsic problem of an observational error and necessity of survey for much more socio-economic data, the traffic volume on all orad and OD pairs in Pusan.

• KDI Journal of Economic Policy
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• 제42권4호
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• pp.117-138
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• 2020

Few studies have specifically focused on the uncertainty of demand forecasting despite the fact that uncertainty is the one of greatest risks for governments and private partners in PPP projects. This study presents a methodology for finding robust contract conditions considering uncertainty in travel demand forecasting in a PPP project. Through a case study of an urban railway PPP project in Korea, this study uncovered the risk of excessive government payments to private partners due to the uncertainty in contracted forecast ridership levels. The results allow the suggestion that robust contract conditions could reduce the expected total level of government payments and lower user fees while maintaining profitability of the project. This study offers a framework that assists contract negotiators and gives them more information regarding financial risks and vulnerabilities and helps them to quantify the likelihood of these vulnerabilities coming into play during PPP projects.

• 한국항만경제학회지
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• 제33권1호
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• pp.75-87
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• 2017

본 연구의 목적은 시스템 다이내믹스를 활용하여 선박 연료유 가격의 중장기 예측분석을 수행하는 것이다. 연료유 가격의 정확한 예측을 위해 가격 결정에 영향을 미치는 다양한 변수들 간의 인과적 관계를 바탕으로 정량화된 모델을 구축하였다. 연료유 가격 결정에는 유가에 영향을 미치는 원유 소비와 생산, 경제변화에 영향을 미치는 GDP, 환율 등과 함께 해운물류시장의 수요와 공급에 의해 결정되는 해상운임 등 다양한 구성변수들을 기반으로 시스템 다이내믹스를 활용한 연료유 가격을 예측하고 MAPEs 등을 통한 객관성을 검증하였다. 본 연구의 분석 결과 2029년까지의 연료유 가격은 2016년 대비 소폭 상승세를 보일 것으로 예상되지만 지난 2012년과 같은 급등세는 나타나지 않을 것으로 전망되었다. 본 연구는 각종 변수들 간의 동적인 인과관계를 활용하여 연료유 가격을 예측하여 합리적 추정결과를 유도할 수 있었다는 점과 가격 결정에 영향을 미치는 다양한 변수들의 구조적 관계를 손쉽게 파악함으로써 연료유 가격 변화에 대한 종합적인 위험 관리가 가능하여 해운기업의 효율적인 선대관리를 지원하는데 가치를 가지고 있다.