• 품질경영학회지
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• 제14권1호
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• pp.53-60
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• 1986

The purpose of this study has been finding where GMDH (Group Method of Data Handling) lies in accordance with comparing other methods and ascertaining the effectiveness of GMDH at the systems of forecasting method. Other methods used for the comparison are: multiple regression model, Brown's third exponential smoothing model. Also the study has reviewed how the expected value and equatior are changed by GMDH. At the same time, the study has also reviewed various characteristics made with comparatively a few data. In conclusion, GMDH is better than the other method in point of view fitness, high effectiveness in self-selection and self-construction of the variables.

• 융합보안논문지
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• 제11권3호
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• pp.19-24
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• 2011

소프트웨어 고장 시간은 테스팅 시간과 관계없이 일정하거나, 단조증가 혹은 단조 감소 추세를 가지고 있다. 이러한 소프트웨어 신뢰모형들을 분석하기 위한 자료척도로 자료에 대한 추세 검정이 개발되어 있다. 추세 분석에는 산술평균 검정과 라플라스 추세 검정 등이 있다. 추세분석들은 전체적인 자료의 개요의 정보만 제공한다. 본 논문에서는 고장시간을 측정하다가 시간 절단이 될 경우에 미래의 고장 시간 예측에 관하여 연구 하였다. 시계열 분석에 이용되는 단순이동 평균법과 가중이동평균법, 지수평활법을 이용하여 미래고장 시간을 예측하여 비교하고자 한다. 실증분석에서는 고장간격 자료를 이용하여 모형들에 대한 예측값을 평균자승오차를 이용하여 비교하고 효율적 모형을 선택 하였다.

• 조명전기설비학회논문지
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• 제27권6호
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• pp.17-22
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• 2013

Fuzzy linear regression method has been used for short-term load forecasting of the special day in the previous researches. However, considerable load forecasting errors would be occurring if a special day is located on Saturday or Monday. In this paper, a new load forecasting method for the consecutive holidays is proposed with the consideration of the power demand variation rate. In the proposed method, a exponential smoothing model reflecting temperature is used to short-term load forecasting for Sunday during the consecutive holidays and then the loads of the special day during the consecutive holidays is calculated using the hourly power demand variation rate between the previous similar consecutive holidays. The proposed method is tested with 10 cases of the consecutive holidays from 2009 to 2012. Test results show that the average accuracy of the proposed method is improved about 2.96% by comparison with the fuzzy linear regression method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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• pp.275-284
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• 2016

정확한 전력수요 예측은 에너지 소비를 줄이고 전력수급의 불균형을 방지한다. 본 연구는 외부요인의 영향을 가장 적게 받는 특정 시간대의 일 단위 전력 수요량을 참조선 (reference line)으로 한 시계열모형을 세우고자 한다. 고려된 시계열모형은 슬라이딩 창을 이용한 이중 계절성 Holt-Winters 모형과 TBATS 모형이다. 시계열모형의 모수는 2009년 1월 4일부터 2011년 12월 31일까지 자료를 이용하여 추정되었으며, 2012년 1월 1일부터 2012년 12월 29일까지의 각 모형의 전력수요량을 예측하여 성능을 비교하였다. RMSE와 MAPE를 통해 예측 성능을 비교한 결과 TBATS 모형의 성능이 우수하였다.

• 전기학회논문지P
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• 제58권4호
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1601-1607
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• 2016

오늘날 기상정보는 도로공학, 경제학, 환경공학 등 다양한 분야에 활용되고 있다. 본 연구는 전력수요 예측을 위한 기상정보 활용성을 평가하고자 한다. 기상변수는 기상관측소에서 수집되는 기온, 풍속, 습도, 운량, 기압과 기온, 풍속, 상대습도의 합성지수인 체감온도와 불쾌지수가 고려되었다. 전력수요 예측을 위한 시계열모형으로 슬라이딩 창 방식의 TBATS 삼중지수평활모형이 고려되었다. 월 단위 기상변수와 전력수요 예측오차간 상관분석 결과를 보면 시간대별로 차이를 있으나 기온, 불쾌지수, 체감온도가 전력수요 예측오차와 상관성이 높았다. 이에 과거 3년의 월단위 전력수요 예측오차와 기상변수의 회귀모형식으로 전력수요 예측값의 편의를 보정하였다. 온도, 상대습도, 풍속으로 TBATS 모형의 전력수요 예측값을 보정한 결과 TBATS 모형에 비해 RMSE가 약 6.1% 줄었다.

• 유통과학연구
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• 제14권11호
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• 한국철도학회논문집
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• 제7권1호
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• pp.9-13
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• 2004

Railway passenger demand forecasts may be used directly, or as inputs to other optimization models use them to produce estimates of other activities. The optimization models require demand forecasts at the most detailed level. In this environment exponential smoothing forecasting methods such as Holt-Winters are appropriate because it is simple and inexpensive in terms of computation. There are several initialization methods for Holt-Winters Model. The purpose of this paper is to compare the initialization methods for Holt-Winters model.

• 한국산림과학회지
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• 제84권2호
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• pp.121-130
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• 1995

미국 미시간주의 여행수요(旅行需要)를 예측(豫測)하기 위하여 사용되어진 여러 모델들의 예측정확성(豫測正確性)이 검토되었다. 8가지의 연년(連年)모델들은 2년까지 예측하는데 그리고 9가지의 분기(分期)모델들은 4분기(分期)까지 예측하는데 사용되어 졌다. 연년(連年)모델의 예측정확성(豫測正確性) 평가(評價)에서, 중회귀(重回歸)모델은 1년과 2년을 예측(豫測)하는데 있어 다른 방법들 보다 더 정확(正確)했다. 분기(分期)모델에 있어서는, Winters' exponential smoothing와 Box-Jenkins 방법이 1 분기예측(分期豫測)에 있어 naive 1 s 보다 더 정확(正確)했으나 2분기(分期), 3분기(分期), 4분기(分期)를 예측(豫測)하는데 이 방법(方法)들은 naive 1 s 보다 정확(正確)하지 않았다. 정교(精巧)한 모델들은 분기별(分期別) 예측(豫測)을 하는데 있어서 단순(單純)한 모델들보다 더 정확(正確)하지 않았다. 연년(連年)모델과 분기(分期)모델을 이용한 1년간(年間) 예측비교(豫測比較)에서, 중회귀모형(重回歸模型)은 연간자료(年間資料)보다 분기자료(分期資料)에 적용(適用)할 때 더 좋은 결과(結果)를 얻었으나 그 차이(差異)가 미약(微弱)하며 다른 모델들은 일관성(一貫性)있게 좋은 결과(結果)를 갖지 않으므로 연년(連年)모델보다 分期모델을 사용하도록 강력하게 권장할 수 없다. 연년(連年)모델은 기대(期待)하였던 것처럼 예측기간(豫測期間)이 길어짐으로서 예측정확성(豫測正確性)이 감소(減少)하였으나 분기(分期)모델은 이같은 결과(結果)를 나타내지 않았다.