• Title, Summary, Keyword: Exponential Smoothing Method

Search Result 109, Processing Time 0.036 seconds

Exponential Smoothing with an Adaptive Response to Random Level Changes (임의의 수준변화에 적절히 반응할 수 있는 지수이동가중평균법)

  • Jun, Duk-Bin
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.16 no.2
    • /
    • 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.

  • PDF

Estimation of Smoothing Constant of Minimum Variance and its Application to Industrial Data

  • Takeyasu, Kazuhiro;Nagao, Kazuko
    • Industrial Engineering and Management Systems
    • /
    • v.7 no.1
    • /
    • 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.

Optimal Coefficient Selection of Exponential Smoothing Model in Short Term Load Forecasting on Weekdays (평일 단기전력수요 예측을 위한 최적의 지수평활화 모델 계수 선정)

  • Song, Kyung-Bin;Kwon, Oh-Sung;Park, Jeong-Do
    • The Transactions of The Korean Institute of Electrical Engineers
    • /
    • v.62 no.2
    • /
    • 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.

Estimation of Smoothing Constant of Minimum Variance and Its Application to Shipping Data with Trend Removal Method

  • Takeyasu, Kazuhiro;Nagata, Keiko;Higuchi, Yuki
    • Industrial Engineering and Management Systems
    • /
    • v.8 no.4
    • /
    • pp.257-263
    • /
    • 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.

Robust Method of Video Contrast Enhancement for Sudden Illumination Changes (급격한 조명 변화에 강건한 동영상 대조비 개선 방법)

  • Park, Jin Wook;Moon, Young Shik
    • Journal of the Institute of Electronics and Information Engineers
    • /
    • v.52 no.11
    • /
    • pp.55-65
    • /
    • 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.

An Algorithm of Short-Term Load Forecasting (단기수요예측 알고리즘)

  • Song Kyung-Bin;Ha Seong-Kwan
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.53 no.10
    • /
    • pp.529-535
    • /
    • 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.

A Forecasting Method for Court Auction Information System using Exponential Smoothing (지수평활을 이용한 법원 경매 정보 시스템의 낙찰가 예측방법)

  • Oh, Kab-Suk
    • Journal of the Korea Society of Computer and Information
    • /
    • v.11 no.5
    • /
    • pp.59-67
    • /
    • 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.

  • PDF

Development of Predictive Smoothing Voter using Exponential Smoothing Method (지수 평활법을 이용한 Predictive Smoothing Voter 개발)

  • Kim, Man-Ho;Lim, Chang-Hwy;Lee, Suk;Lee, Kyung-Chang
    • Transactions of the Korean Society of Automotive Engineers
    • /
    • v.14 no.6
    • /
    • pp.34-42
    • /
    • 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.

Computation and Smoothing Parameter Selection In Penalized Likelihood Regression

  • Kim Young-Ju
    • Communications for Statistical Applications and Methods
    • /
    • v.12 no.3
    • /
    • 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.

Forecasting of Stream Qualities at Gumi industrial complex by Winters' Exponential Smoothing

  • Song, Phil-Jun;Um, Hee-Jung;Kim, Jong-Tae
    • Journal of the Korean Data and Information Science Society
    • /
    • v.19 no.4
    • /
    • pp.1133-1140
    • /
    • 2008
  • The goal of this paper is to analysis of the trend for stream quality in Gumi industrial complex with Winters' exponential smoothing method. It used the five different monthly time series data such as BOD, COD, TN, TP and EC from January 1998 to December 2006. The data of BOD, COD, TN, TP and EC are analyzed by time series method and forecasted the trends until December 2007. The stream qualities change for the better about BOD, COD, TN and TP, but the stream qualities resulted by EC is still serious.

  • PDF