• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.137-146
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• 2005

Neural network is one of the most notable technique. It usually provides more powerful forecasting models than the traditional time series techniques. Employing the Ensemble technique in forecasting model, one should provide a initial distribution. Usually the uniform distribution is assumed so that the initialization is noninformative. However, it would be expected a sequential informative initialization based on data rather than the uniform initialization gives further reduction in forecasting error. In this note, a modified Ensemble algorithm using sequential initial probability is developed. The sequential distribution is designed to have much weight on the recent data.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.281-293
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• 2022

Time series analysis was performed on data from 2009 to 2018 from the Chuncheon groundwater observation network to understand the characteristics of groundwater level fluctuations in the network. There are five observatories, all of which are installed in rock aquifers, and periodic inspections and management are performed by the relevant operating organization. Auto-correlation, spectral density, and cross-correlation analysis was performed.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.39-48
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• 2011

To predict the shelf-life of ammunition stockpiled in intermediate have practical meaning as a core value of combat support. This research is to Predict the shelf-life of ammunition by applying time series analysis based on report from ASRP of the 155mm, KD541 performed for 6 years. This study applied time series analysis using 'Mini-tab program' to measure the amount of stabilizer as time passes by is different from the other one that uses regression analysis. The average shelf-life of KD541 drawn by time series analysis was 43 years and the lowest shelf-life assessed on the 95% confidence level was 35 years.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.397-404
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• 1999

1997년말 발생한 외환위기 이후 불확실성의 증대로 시계열모형을 이용한 경제예측에 한계가 노정되고 있다. 이를 극복하기 위하여 경제주체의 기대(expectation)를 파악할수 있는 기업경기실사지수를 경제예측에 도입할 필요가 있다. 본고에서는 기업경기실사지수를 이용한 모형과 시계열모형을 추정하고 이들을 예측력 측면에서 비교, 분석해보았다. 분석결과 불확실성이 높았던 외환위기이후 기간에는 기업경기실사지수를 이용한 모형이 시계열모형보다 예측력면에서 우수한 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.479-490
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• 1999

선형시계열모형인 AR(1)모형과 비선형시계열모형인 RCA(1), ARCH(1)모형에서 이상치(Outlier)가 존재할 경우 최소제곱추정량과 M추정량간의 점근상대효율(Asymptotic Relative Efficiency: ARE)을 구하여 두 추정량의 로버스트 성질을 비교·분석하였다. 또한 여러 유계함수(Huber, Tukey, Andrews, Hampel)들을 M추정함수에 적용하여 각각의 유계함수들을 비교·분석하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.1106-1108
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• 2010

이 연구에서는 유전자알고리즘과 인공신경망의 특성을 결합한 유전자신경망모형에 대하여 논의한다. 이 모형을 이용하여 단기 시계열자료를 예측한다. 그 예측 결과는 유전자신경망모형이 역전파 신경망모형에서 보다 더 작은 예측오차를 보였다. 역전파 신경망보다 더 효과적임을 보임으로써 유전자신경망모형을 이용한 시계열자료 예측이 보다 효율적인 방법임을 제시한다.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.111-120
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• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.799-803
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• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.23-26
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• 2006

기존의 데이터 마이닝 예측 기법 중 회귀분석은 학습 단계에서 생성된 모델을 변경 없이 새로운 데이터에 적용하였다. 그러나 시계열 데이터에 모델 변경 없이 동일하게 적용하면 시간이 지남에 따라 정확도가 낮아지는 단점이 있다. 따라서 이 논문에서는 시간에 따라 변화하는 시계열데이터의 특성을 고려하여 점진적으로 회귀 모델을 갱신하는 기법을 제안한다. 이 기법은 입력되는 모든 데이터를 회귀 모델에 적용하여 점진적으로 모델을 갱신한다. 제안된 기법의 타당성은 RME(Relative Mean Error)와 RMSE(Root Mean Square Error)를 이용하여 측정하였다. 정확도 측정 실험 결과 제안 기법인 IMQR(Incremental Multiple Quadratic Regression) 기법이 MLR(Multiple Linear Regression), MQR(Multiple Quadratic Regression), SVR(Support Vector Regression) 기법에 비해 RME 가 평균 2%, RMSE 가 평균 0.02 정도 우수한 결과를 얻었다.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.369-377
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• 2006

This paper considers moving variance ratio (MVR) for valiance detection problem with time series data in progress. For testing purpose, parametric method based on F distribution and nonparametric method based on empirical distribution are compared via simulation study.