• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1995년도 추계학술대회발표논문집; 서울대학교, 서울; 30 Sep. 1995
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• pp.334-343
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• 1995

In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2001년도 학술발표회 논문집(I)
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• pp.45-51
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• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1187-1198
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• 2019

In this study, we propose a new $PM_{10}$ forecasting model for Seoul region using DNN(Deep Neural Network) and secondary data. The previous numerical and Julian forecast model have been developed using primary data such as weather and air quality measurements. These models give excellent results for accuracy and false alarms, but POD is not good for the daily life usage. To solve this problem, we develop four secondary factors composed with primary data, which reflect the correlations between primary factors and high $PM_{10}$ concentrations. The proposed 4 models are A(Anomaly), BT(Back trajectory), CB(Contribution), CS(Cosine similarity), and ALL(model using all 4 secondary data). Among them, model ALL shows the best performance in all indicators, especially the PODs are improved.

• International Journal of Contents
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• 제17권3호
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• pp.1-14
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• 2021

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

• 자원ㆍ환경경제연구
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• 제9권2호
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• pp.333-347
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• 2000

We employed various econometic methods to estimate the production index elasticity and the price elasticity of elecricity demand in Korea and compared the forecasting power of those methods. Cointegration models (ADL model, Engle-Granger model, Full Informtion Maximum Likelihood method by Johansen and Juselius) and Dynamic OLS by Stock and Watson were considered. The forecasting power test shows that Dynamic OLS has the best forecasting power. According to Dynamic OLS, the production index elasticity and the price elasticity of electricity demand in Korea are 0.13 and -0.40, respectively.

• 산업경영시스템학회지
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• 제7권10호
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• pp.47-51
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• 1984

In many forecasting problem, there is little or no useful historical information available at the time the initial forecast is required, The propose of this paper is study on Bayesian Method in forecasting. I : Introduction. II : Bayesian estimation. III : Constant Model. IV : General time series Models. V : Conclusion. The Bayesian procedure are then used to revise parameter estimates when time series information is available, in this paper we give a general description of the bayesian approach and demonstrate the methodology with several specific cases.

• 한국유기농업학회지
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• 제30권2호
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• pp.231-239
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• 2022

The purpose of this study is to provide meaningful information for various stakeholders' decision-making process through forecasting of domestic beef demand. Three different exponential smoothing models were evaluated, and a double exponential smoothing model was used to forecast domestic beef demand based on time-series data, As a result of the forecast, domestic beef consumption is expected to increase by 37,000 to 40,000 tons per year from 2020 to 2025.

• 한국산학기술학회논문지
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• 제10권10호
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• pp.2923-2934
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• 2009

본 논문에서는 차세대 디스플레이 기술에 대해서 기술의 추세를 예측하였다. 차세대 디스플레이 기술은 최근에 급부상 하는 기술로 연구개발을 수행하는데 있어 추후 연구 방향을 설정하고 기술전략을 세우는데 불확실성을 줄여주기 위해 기술의 미래 발전 방향이나 추세에 대해서 예측을 해 보는 것이 중요하다. 이렇게 함으로써 연구개발 목표를 좀 더 명확히 설정할 수 있고 불필요한 투자를 방지할 수 있다. 본 논문에서는 차세대 디스플레이 기술에 대해서 특허 데이터를 사용하여 정량적인 예측을 수행하였다. 예측 방법으로는 Gompertz, Logistic, Bass모형을 사용하였다. 이 세 모형들은 과거 시장에서의 제품의 확산과정을 설명하는데 사용되었던 모형이다. Gompertz, Logistic모형은 시장의 수요예측 뿐만 아니라 기술의 수요예측에도 주로 쓰였기 때문에 본 논문에서도 이 두 모형을 적용하였다. 하지만 Gompertz, Logistic 모형은 시장에서의 내부 효과에 의한 확산만을 반영한 모형이고 성장의 상한 값을 추정하는 데 있어 추정이 쉽지 않다는 단점이 있다. 기술의 수요도 시장에서의 제품의 확산처럼 기술혁신에 의한 외부 효과와 산업으로 전파될 때의 내부효과가 함께 수요의 확산에 영향을 끼칠 것이라고 판단하여 본 논문에서는 시장에서의 제품 확산의 외부효과와 내부효과를 동시에 고려한 Bass모형도 함께 적용하여 예측을 수행하였다. 또한 Gompertz, Logistic 모형의 상한 값을 Bass모형을 통해 객관적으로 추정하여 예측을 수행함으로써 두 모형의 단점을 보완하였다.