• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.129-134
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• 1990

Exponential smoothing methods have enjoyed a long history of successful applications and have been used in forecasting for many years. However, it has been long known that one of the deficiencies of the method is an inability to respond quickly to interventions to interruptions, or to large changes in level of the underlying process. An exponential smoothing method adaptive to repeated random level changes is proposed using a change-detection statistic derived from a simple dynamic linear model. The results are compared with Trigg and Leach's and the exponential smoothing methods.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.44-50
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• 2008

Focusing on the exponential smoothing method equivalent to (1, 1) order ARMA model equation, a new method of estimating smoothing constant using exponential smoothing method is proposed. This study goes beyond the usual method of arbitrarily selecting a smoothing constant. First, an estimation of the ARMA model parameter was made and then, the smoothing constants. The empirical example shows that the theoretical solution satisfies minimum variance of forecasting error. The new method was also applied to the stock market price of electrical machinery industry (6 major companies in Japan) and forecasting was accomplished. Comparing the results of the two methods, the new method appears to be better than the ARIMA model. The result of the new method is apparently good in 4 company data and is nearly the same in 2 company data. The example provided shows that the new method is much simpler to handle than ARIMA model. Therefore, the proposed method would be better in these general cases. The effectiveness of this method should be examined in various cases.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.55-65
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• 2015

Contrast enhancement methods for a single image applied to videos may cause flickering artifacts because these methods do not consider continuity of videos. On the other hands, methods considering the continuity of videos can reduce flickering artifacts but it may cause unnecessary fade-in/out artifacts when the intensity of videos changes abruptly. In this paper, we propose a robust method of video contrast enhancement for sudden illumination changes. The proposed method enhances each frame by Fast Gray-Level Grouping(FGLG) and considers the continuity of videos by an exponential smoothing filter. The proposed method calculates the smoothing factor of an exponential smoothing filter using a sigmoid function and applies to each frame to reduce unnecessary fade-in/out effects. In the experiment, 6 measurements are used for the performance analysis of the proposed method and traditional methods. Through the experiment. it has been shown that the proposed method demonstrates the best quantitative performance of MSSIM and Flickering score and show the adaptive enhancement under sudden illumination change through the visual quality comparison.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.59-67
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• 2006

This paper proposes a forecasting method for court auction information system using exponential smoothing. The system forecast a highest bid price for claim analysis, and it is designed to offer an quota information by the bid price. For this realization, we implemented input interface of object data and web interface of information support. Input interface can be input, update and delete function and web interface is support some information of court auction object. We propose a forecasting method using exponential smoothing of a highest bid price for auto-claim analysis with real time information support and the results are verified the feasibility of the proposed method by experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.6
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• pp.34-42
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• 2006

As many systems depend on electronics, concern for fault tolerance is growing rapidly. For example, a car with its steering controlled by electronics and no mechanical linkage from steering wheel to front tires(steer-by-wire) should be fault tolerant because a failure can come without any warning and its effect is devastating. In order to make system fault tolerant, there has been a body of research mainly from aerospace field. This paper presents the structure of predictive smoothing voter that can filter out most erroneous values and noise. In addition, several numerical simulation results are given where the predictive smoothing voter outperforms well-known average and median voters.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.743-758
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• 2005

This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.

• Korea Trade Review
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• v.44 no.2
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• pp.51-62
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• 2019

This study predicts the future world seaborne trade volume with econometrics methods using 23-year time series data provided by Clarksons. For this purpose, this study uses simple regression analysis, exponential smoothing method and seemingly unrelated regression model (SUR Model). This study is meaningful in that it predicts worldwide total seaborne trade volume and seaborne traffic in four major items (container, bulk, crude oil, and LNG) from 2019 to 2023 as there are few prior studies that predict future seaborne traffic using recent data. It is expected that more useful references can be provided to trade related workers if the analysis period was increased and additional variables could be included in future studies.