• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1235-1243
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• 2015

Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.

• Health Policy and Management
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• v.10 no.2
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• pp.1-21
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• 2000

This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.55-59
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• 2022

This paper introduce Renewable Energy forecasting technology, which is a part of renewable management system. Then, calculation method of dedicated transmission line's meteorological data to forecast renewable energy is suggested. As the case of dedicated transmission line, there is only power output data combined the number of renewable plants' output that acquired from circuit breakers. So it is need to calculate meteorological data for dedicated transmission line that matched combined power output data. this paper suggests two calculation method. First method is select the plant has the largest capacity, and use it's meteorological data as line meteorological data. Second method is average with weight that given according to plants' capacity. In case study, suggested methods are applied to real data. Then use calculated data to Renewable forecasting and analyze the forecasting results.

• Journal of the military operations research society of Korea
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• v.2 no.1
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• pp.111-125
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• 1976

This paper is to explore technological forecasting methodologies and their application to military R&D programming. Among a number of forecasting methodologies, eight frequently used methods are explained. They are; Delphi method, analogy, growth curve, trend extrapolation, analytical model, breakthrough, normative method, and combined method. Due to the characteristic situation of a developing country, the application of technological forecasting to the Korean military R&D programming is limited. Therefore, only two forecasting methods such as Delphi and normative method are utilized in the development of a decision model for the military R&D programming. The model consists of a dynamic programming using decision tree model, which optimizes the total cost to equip a certain military item under a given range of risk during a given period. Some pitfalls in forecasting methodologies and of the model are discussed.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.931-954
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• 2003

It is widely recognized that economic time series involved not only the linearities but also the non-linearities. In this paper, when the economic time series data have the nonlinear characteristics we propose the forecasts method using combinations of both forecasts from linear and nonlinear models. In empirical study, we compare the forecasting performance of 4 exchange rates models(AR, GARCH, AR+GARCH, Bilinear model) and combination of these forecasts for dairly Won/Dollar exchange rates returns. The combination method is selected by the estimated individual forecast errors using Monte Carlo simulations. And this study shows that the combined forecasts using unrestricted least squares method is performed substantially better than any other combined forecasts or individual forecasts.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.451-454
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• 2014

Short-term load forecasting(STLF) for electric power demand is essential for stable power system operation and efficient power market operation. We improved STLF method by using hourly temperature as an input data. In order to using hourly temperature to STLF algorithm, we calculated temperature-electric power demand sensitivity through past actual data and combined this sensitivity to exponential smoothing method which is one of the STLF method. The proposed method is verified by case study for a week. The result of case study shows that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Korea journal of population studies
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• v.34 no.3
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• pp.157-177
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• 2011

This paper examines the performance of the coherent Lee-Carter method for the mortality forecasting for the Republic of Korea combined with Japan and the Taiwan Province of China as a group by comparing it with the separately applied Lee-Carter method. It narrowed the gap of life expectancies between three countries from 6.8 years to 3.0 years in 2050, with higher life expectancy forecasts for the Taiwan Province of China and lower ones for Japan than with the separate forecast. This method did not affect the sex-combined life expectancy forecast for the Republic of Korea, but it accelerated the mortality decline for ages 65 and over and decelerated it for the younger age groups, diminishing sex differentials of life expectancy at a slower speed. It suggests that the integration of regional mortality information into mortality forecasting of one country gives several advantages in terms of short run fit within each country as well as long run convergence between countries, a modification of the age pattern of mortality decline, and a consistent application of the forecasting of subgroups within a country.

• Journal of KSNVE
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• v.2 no.4
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• pp.293-304
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• 1992

Active vibration control is presented with simulation and experiment. Dynamic Data System(DDS) method is used for system modeling and this model is combined with an forecasting control technique to derive a control equation. In the experiment, on-line digital computer monitors structural vibration and calculates control input. The control input is sent to an electromagnetic actuator which cancels the structural vibration. Experiment is performed first with a simple beam setup to demonstrate the effetiveness of this method. This method is then applied to a color laser printer to actively modify the structure. The beam experiment showed vibration reduction of over 60% with one-and two-DOF models. In the printer structure experiment, the first mode of 308 Hz was successfully controlled with a one-DOF model.

• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.47-68
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• 1997

This paper discusses a method of modified Elman network(1990) for nonlinear predictions and its a, pp.ication to forecasting daily exchange rate returns. The method consists of two stages that take advantages of both time domain filter and modified feedback networks. The first stage straightforwardly employs the filtering technique to remove extreme noise. In the second stage neural networks are designed to take the feedback from both hidden-layer units and the deviation of outputs from target values during learning. This combined feedback can be exploited to transfer unconsidered information on errors into the network system and, consequently, would improve predictions. The method a, pp.ars to dominate linear ARMA models and standard dynamic neural networks in one-step-ahead forecasting exchange rate returns.