• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.09a
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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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• v.9 no.4
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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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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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.

• Journal of Korea Multimedia Society
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• v.22 no.10
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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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• v.17 no.3
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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.

• Environmental and Resource Economics Review
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• v.9 no.2
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.7 no.10
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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.

• Korean Journal of Organic Agriculture
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• v.30 no.2
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2923-2934
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• 2009

This paper presents study on technological forecasting of Next-Generation Display technology. Next-Generation Display technology is one of the emerging technologies lately. So databases on patent documents of this technology were analyzed first. And patent analysis was performed for finding out present technology trend. And the forecast for this technology was made by growth curves which were obtained from forecast models using patent documents. In previous study, Gompertz, Logistic, Bass were used for forecasting diffusion of demand in market. Gompertz, Logistic models which were often used for technological forecasting, too. So, two models were applied in this study. But Gompertz, Logistic models only consider internal effect of diffusion. And it is difficult to estimate maximum value of growth in two models. So, Bass model which considers both internal effect and external effect of diffusion was also applied. And maximum value of growth in Gompertz, Logistic models was estimated by Bass model.