• 대한산업공학회지
• /
• 제16권2호
• /
• pp.129-134
• /
• 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
• /
• 제7권1호
• /
• pp.44-50
• /
• 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.

• 전자공학회논문지
• /
• 제52권11호
• /
• pp.55-65
• /
• 2015

동영상 대조비 개선 과정에서 단일 영상을 위해 연구된 대조비 개선 방법들을 사용할 수 있지만, 동영상의 연속성이 고려되지 않으면 원본 동영상에 없는 깜박임을 야기할 수 있다. 또한 동영상의 연속성을 고려하는 경우, 깜박임은 억제할 수 있지만 연속성 때문에 조명의 급격한 변화할 때 불필요한 페이드인/아웃(fade-in/out) 현상이 발생하는 단점이 발생할 수 있다. 본 논문에서는 깜박임과 페이드인/아웃 현상 없이 동영상의 대조비를 개선하는 방법을 제안한다. 제안하는 방법은 Fast Gray-Level Grouping(FGLG)를 사용하여 각 프레임의 대조비를 개선하고, 깜박임을 억제하기 위해 Exponential smoothing 필터를 사용한다. 불필요한 페이드인/아웃 현상을 억제하기 위해서는 S형 함수로 Exponential smoothing 필터의 평활화 비율을 프레임 별로 적응적으로 계산하여 적용한다. 실험에서 제안하는 방법과 기존의 방법들은 6가지 측정 기준을 적용하여 성능을 비교 및 분석한다. 실험 결과, 제안하는 방법은 영상 형태 보존을 측정하는 MSSIM과 깜박임을 측정하는 Flickering score에서 정량적으로 가장 높은 결과를 보여주었으며, 시각적인 품질 비교를 통해 조명 변화에 따른 적응적인 개선을 정성적 결과로 입증하였다.

• Communications for Statistical Applications and Methods
• /
• 제12권3호
• /
• pp.743-758
• /
• 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.

• 산업공학
• /
• 제18권3호
• /
• pp.343-349
• /
• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• 한국방재안전학회논문집
• /
• 제14권2호
• /
• pp.35-42
• /
• 2021

본 논문은 전기자동차 충전시설 확충계획에 중요한 요소인 전기자동차 전력 수요량 예측정보를 생산하기 위하여 Exponential Smoothing를 이용하여 전력 수요량 예측 모형을 제안하였다. 모형의 입력자료 구축을 위하여 종속변수로 월별 시군구 전력수요량을 독립변수로 월별 시군구 충전소 보급대수, 월별 시군구 전기자동차 충전소 충전 횟수, 월별 전기자동차 등록대수 자료를 월 단위로 수집하고 수집된 7년간 자료 중 4년간 자료를 학습기간으로 3년간 자료를 검증 기간으로 적용하였다. 전기자동차 전력 수요량 예측 모형의 정확성을 검증하기위하여 통계적 방법인 Exponential Smoothing(ETS), ARIMA모형의 결과와 비교한 결과 ETS, ARIMA 각각의 오차율은 12%, 21%로 본 논문에서 제시한 ETS가 9% 더 정확하게 분석되었으며, 전기자동차 전력 수요량 예측 모형으로써 적합함을 확인하였다. 향후 이 모형을 이용한 전기자동차 충전소 설치 계획부터 운영관리 측면에서 활용될 것으로 기대한다.

• 응용통계연구
• /
• 제33권3호
• /
• pp.257-267
• /
• 2020

본 논문은 단변량 지수평활법의 확장된 형태인 다변량 지수평활법을 소개하고 다변량 시계열 분석에 활용한다. 다변량 지수평활법은 한 개의 오차를 기반으로 하는 상태공간모형을 이용하여 추정의 편리성을 제고하며, 다변량 시계열간의 잠재적인 상호연관성을 활용하여 적합도 및 예측력을 향상시킨다. 다변량 지수평활법의 성능을 평가하기 위하여 월별 원/달러 및 원/파운드 환율자료를 분석하고 예측한다. 대안 모형의 예측 결과와 비교하여 다변량 지수평활법의 우수성을 확인한다.

• 전기학회논문지
• /
• 제62권2호
• /
• pp.149-154
• /
• 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.

• 무역학회지
• /
• 제44권2호
• /
• pp.51-62
• /
• 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.

• 산업경영시스템학회지
• /
• 제17권31호
• /
• pp.1-10
• /
• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.