• 물과 미래
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• 제12권2호
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• pp.63-69
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• 1979

The sequences of monthly streamflows constitute a non-statonary time series. The purely stochastic model has been applied to data generation of non-stationary time series. Tow different mothods--single site and multisite generation--have been used on the hydrologic time series. In this study the synthetic generation method by bivariate analysis, studied by Thomas Fiering, one of multi-site models, has been applied to the historical data on monthly streamflows at two sites in Nakdong River, and also for validity of this model the single site Thomas Fiering model applied. Through statistical analysis it has been shown that the performance of bivariate Thomas Fiering model was better than that of the other. By comparison of mean and standard deviaion between the historical and the generated, and cross correlogram interpretation, it has been known that the model used herein has good performance to simultaneously generate the monthly streamflows at two sites in a river hasin.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• 응용통계연구
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• 제20권1호
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• pp.53-60
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• 2007

시계열 자료 분석에서 ARCH류와 같은 조건부 이분산성 모형을 가정하고 분석하는 모형들이 많이 쓰이고 있다. 실제 우리나라 금융 시계열 자료들을 분석해 보면 비정상성을 나타내는 경우가 드물지 않게 나타난다. 즉, 단위근 형태의 비정상 패턴(integrated phenomenon)에 가까운 경우가 자주 나타난다. 본 논문에서는 다양한 국내 금융시계열 15개에(주가지수, 선물지수, 환율, 이자율 등) GARCH(1,1) 모형을 적합시켜 분산의 지속성을 확인하고, 각 데이터에 첨도(Kurtosis)와 적합된 IGARCH(1,1) 모형을 제시하고자 한다.

• 산업기술연구
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• 제28권B호
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• 산업경영시스템학회지
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• 제35권2호
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• pp.106-112
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• 2012

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in case-by-case manner. In our study, we evaluate whether Hilbert-Huang Transform, a new tool of time-series analysis can be used for effective analysis of such data. It is divided into two points : 1) how effective it is in finding periodic components, 2) whether we can use its results directly in detecting values outside control limits, for which a traditional method such as ARIMA had been used. We use glass furnace temperature data to illustrate the method.

• 산업경영시스템학회지
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• 제37권4호
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• pp.35-41
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• 2014

Real-life time series characteristic data has significant amount of non-stationary components, especially periodic components in nature. Extracting such components has required many ad-hoc techniques with external parameters set by users in a case-by-case manner. In this study, we used Empirical Mode Decomposition Method from Hilbert-Huang Transform to extract them in a systematic manner with least number of ad-hoc parameters set by users. After the periodic components are removed, the remaining time-series data can be analyzed with traditional methods such as ARIMA model. Then we suggest a different way of setting control chart limits for characteristic data with periodic components in addition to ARIMA components.

• 한국방사선학회논문지
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• 제7권1호
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• pp.25-30
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• 2013

본 논문은 1998년부터 2011년까지 강릉 지역의 지방 방사능 측정소에서 측정된 공간 감마선량률의 통계 적인 성질을 조사하였다. Autocorrelation Function Analysis(ACF), Rescaled Range Analysis(R/S Analysis), Detrended Fluctuation Analysis(DFA)의 방법들이 사용되었으며, 이 중 DFA는 non-stationary한 시계열의 장거리 상관성을 보여주는 좋은 방법으로 알려져 있다. 우리는 이 연구를 통해 다음의 사실을 알았다. 첫 번째, 공간 감마선량률은 두 가지 다른 경향을 갖는 크로스 오버가 나타난다. 이것은 연중 공간 감마선량률은 강한 장기 기억 특성이 나타나는데 비해 연간으로 넘어가면 상관성이 사라지는 것을 의미한다. 두 번째, 각 분석 방법들의 지수들이 있는데 이 지수들 사이의 관계식이 맞음을 확인 하였다.

• 경영과학
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• 제24권1호
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• pp.25-34
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• 2007

In this study, we consider stationary waiting times in finite-buffer 3-node single-server queues in series with a Poisson arrival process and with either constant or non-overlapping service times. We assume that each node has a finite buffer except for the first node. The explicit expressions of waiting times in all areas of the stochastic system were driven as functions of finite buffer capacities. These explicit forms show that a system sojourn time does not depend on the finite buffer sizes, and also allow one to compute and compare characteristics of stationary waiting times at all areas under two blocking rules communication and manufacturing blocking. The goal of this study is to apply these results to an optimization problem which determines the smallest buffer capacities satisfying predetermined probabilistic constraints on stationary waiting times at all nodes. Numerical examples are also provided.

• ETRI Journal
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• 제31권1호
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• pp.86-88
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• 2009

In this letter, we consider an m-node tandem queue (queues in series) with a Poisson arrival process and either deterministic or non-overlapping service times. With the assumption that each node has a finite buffer except for the first node, we show the non-increasing convex property of stationary waiting time with respect to the finite buffer capacities. We apply it to an optimization problem which determines the smallest buffer capacities subject to probabilistic constraints on stationary waiting times.

• 한국대기환경학회지
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• 제4권2호
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• pp.72-81
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• 1988

The typical ARIMA model which was developed by Box and Jenkins, was applied to the monthly $SO_2$ data collected at Seoungsoo and Oryudong in metropolitan area over five years, 1982 to 1986. To find out the changing pattern of $SO_2$ concentration, autocorrelation and partial autocorrelation analysis were undertaken. The three steps of time series model building were followed and the residual series was found to be a random white noise. The results of this study is summarized as follows. 1) The monthly $SO_2$ series was found to be a non-stationary series which which has a periodicity of 12 months. After eliminating the periodicity by differencing, the monthly $SO_2$ series became a stationary series. 2) The ARIMA seasonal model of the $SO_2$ was determined to be ARIMA $(1, 0, 0)(0, 1, 0,)_{12}$ model. 3) The model equations based on the prediction were: for Seoungsoodong: $Y_t = 0.5214Y_{t-1} + Y_{t-12} - 0.5214Y_{t-13} + a_t$ for Oryudong: $Y_t = 0.8549Y_{t-1} + Y_{t-12} - 0.8549Y_{t-13} + a_t$ 4) The validity of the model identified was checked by compairing the measured $SO_2$ values and one-month-ahead predicted values. The result of correlation and regression analysis is as follows. Seoungsoodong: $Y = 0.8710X + 0.0062 r = 0.8768$ Oryudong : $Y = 0.8758X + 0.0073 r = 0.9512$