• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전력기술부문
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• pp.35-38
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• 2005

This paper suggests the method that forecast Dynamic Line Rating (DLR). Thermal Overload Risk (TOR) of next time is forecasted based on current weather condition and DLR value by Monte Carlo Simulation (MCS). To model weather element of transmission line for MCS, we will propose the use of weather forecast system and statistical models that time series law is applied. Also, through case study, forecasted TOR probability confirmed can utilize by standard that decide DLR of next time. In short, proposed method may be used usefully to keep safety of transmission line and reliability of supply of electric Power by forecasting transmission capacity of next time.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.

• 전자통신동향분석
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• 제36권4호
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• pp.145-153
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• 2021

Data imputation is a crucial issue in data analysis because quality data are highly correlated with the performance of AI models. Particularly, it is difficult to collect quality time-series data for uncertain situations (for example, electricity blackout, delays for network conditions). Thus, it is necessary to research effective methods of time-series data imputation. Many studies on time-series data imputation can be divided into 5 parts, including statistical based, matrix-based, regression-based, deep learning (RNN and GAN) based methodologies. This study reviews and organizes these methodologies. Recently, deep learning-based imputation methods are developed and show excellent performance. However, it is associated to some computational problems that make it difficult to use in real-time system. Thus, the direction of future work is to develop low computational but high-performance imputation methods for application in the real field.

• 한국수자원학회논문집
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• 제48권12호
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• pp.981-993
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• 2015

수문 시계열의 분석은 수문자료를 활용한 수자원의 효율적인 운영 및 관리에 필수적인 부분이며, 특히 장기적인 수문량 예측에 널리 활용되고 있다. 이러한 수문 시계열 분석은 전통적으로 하나의 자료계열을 하나의 요인으로 파악하여 자료를 분석하고 예측해왔지만 시계열 자료가 여러 가지 요인으로 혼합되 어 하나의 자료계열로 나타내질 수 있다는 가정 하에 각 요인들을 분해하여 분석하는 방법도 널리 연구되고 있다. 본 연구에서는 경험적 모드분해법을 이용하여 주어진 수문 시계열을 다중 성분으로 분해하고 분해된 각 요소를 시계열 모형으로 재구축한 후, 구축된 요소별 시계열 모형으로부터 예측된 값을 합하여 시계열을 예측하는 방법을 이용하였으며 이를 국내 댐 유입량에 적용한 후 그 결과를 나타내었다. 기존 시계열 모형과 경험적 모드분해법을 이용한 방법의 정확도를 비교한 결과, 기존의 시계열 모형을 이용하여 자료를 예측한 결과보다 경험적 모드분해법을 적용하여 자료를 분해한 후 시계열 자료를 예측한 결과가 주어진 시계열 자료를 더 잘 나타내는 것을 알 수 있었다.

• 한국대기환경학회지
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• 제23권3호
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• pp.364-372
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• 2007

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. In this article, two time series ARE models, the direct ARE model and applied ARE model have been considered for analyzing the ozone data at southern part of the Gyeonggi-Do, Pyeongtaek, Osan and Suwon monitoring sites in Korea. The result shows that the direct ARE model is better suited for describing the ozone concentration in all three sites. In both of the ARE models, eight meteorological variables and four pollution variables are used as the explanatory variables. Also the high level of ozone data (over 80 ppb) have been analyzed at the Pyeongtaek, Osan and Suwon monitoring sites.

• Journal of applied mathematics & informatics
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• 제27권1_2호
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• pp.315-326
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• 2009

The unknown parameters in regression models are usually estimated by using various existing methods. There are several existing methods, such as the least squares method, which is the most common one, the least absolute deviation method, the regression quantile method, and the asymmetric least squares method. For the nonlinear time series regression models, which do not satisfy the general conditions, we will compare them in two ways: 1) a theoretical comparison in the asymptotic sense and 2) an empirical comparison using Monte Carlo simulation for a small sample size.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.31-40
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• 2015

This study proposes a mathematical model that can forecast national defense expenditures. The ongoing European debt crisis weighs heavily on markets; consequently, government spending in many countries will be constrained. However, a forecasting model to predict military spending is acutely needed for South Korea because security threats still exist and the estimation of military spending at a reasonable level is closely related to economic growth. This study establishes two models: an Auto-Regressive Moving Average model (ARIMA) based on past military expenditures and Transfer Function model with the Gross Domestic Product (GDP), exchange rate and consumer price index as input time series. The proposed models use defense spending data as of 2012 to create defense expenditure forecasts up to 2025.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.37-47
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• 2006

Recently, as a result of the growing interest in modeling stationary processes with discrete marginal distributions, several models for integer valued time series have been proposed in the literature. One of these models is the integer-valued autoregressive (INAR) models. However, when modeling with integer-valued autoregressive processes, the distributional properties of forecasts have been not yet discovered due to the difficulty in handling the Steutal Van Ham thinning operator 'o' (Steutal and van Ham, 1979). In this study, we derive the mean squared error of h-step-ahead prediction from a Poisson INAR(1) process, reflecting the effect of the variability of parameter estimates in the prediction mean squared error.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• 응용통계연구
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• 제17권2호
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• pp.219-228
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• 2004

오차항의 분포가 정규분포에 따르지 않는 비선형 시계열인 ARCH모형의 예측구간을 설정하는데 붓스트랩 방법과 근사적 방법간의 포함비율에 대한 정확성을 비교한다. 이 때 모형에서 모수를 추정하는 방법으로서는 분포에 대한 가정을 필요로 하지 않는 quasi-score 추정함수를 이용한 추정 법과 로버스트 추정 함수인 M quasi-score 추정 함수를 이용한 추정법을 사용한다. 추정된 모수를 이용하여 예측구간의 정확성을 비교하고 마지막으로 소비자 물가지수 자료를 이용하여 실제 예측구간을 구하는데 적용한다.