• 한국정보통신학회논문지
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• 제13권8호
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• pp.1580-1586
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• 2009

웨이브릿 변환은 신호처리, 디지털 통신 등 여러 분야에 널리 사용된다. 본 논문에서는 웨이브릿 편이 변조(WSK : wavelet shift keying) 시스템에서 하러(Haar)와 도비치(Daubechies) 웨이브릿 계열(series)을 중심으로 웨이브릿 종류에 대한 성능을 분석한다. 사용된 웨이브릿은 하러, 도비치 4탭, 8탭, 12탭을 사용하였다. 분석방법은 눈 모양에 의한 방법과 에러확률에 의한 방법을 사용하였다. 모의실험 결과 필터계수의 개수가 적을수록 좋은 성능을 보였다.

• 응용통계연구
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• 제31권6호
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• pp.707-719
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• 2018

시계열 데이터의 분류와 군집화를 효율적으로 수행하기 위해 다양한 시계열 표현 방법들이 제안되었다. 본 연구는 Lin 등 (2007)이 제안한 국소 평균 근사를 이용하여 시계열의 차원을 축소한 후 심볼릭 자료로 이산화하는 symbolic aggregate approximation (SAX) 방법의 개선에 대해서 연구하였다. SAX는 국소 평균 근사를 할 때 등간격으로 임의의 개수의 세그먼트로 나누어 평균을 계산하여 세그먼트의 개수에 그 성능이 크게 좌우된다. 따라서 본 논문은 불균형 Haar 웨이블릿 변환을 통해 국소 평균 수준을 등간격이 아니라 자료의 특성을 반영하여 자료 의존적으로 선택하게 함으로써 시계열의 차원을 효과적으로 축소함과 동시에 정보의 손실을 줄이는 방법에 대해서 제안한다. 제안한 방법은 실증 자료 분석을 통해 SAX 방법을 개선시킴을 확인하였다.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.161-168
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• 2004

A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.

• 한국환경과학회지
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• 제24권8호
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• 한국수자원학회논문집
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• 제38권6호
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• pp.439-448
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• 2005

본 논문은 수문시계열에서 나타나는 주기성 및 경향성 등을 평가하기 위한 방법으로 Fourier Transform을 개선한 Wavelet Transform방법을 제시하고 이에 대한 타당성 및 적용성을 월강수량 및 연강수량 자료와 대표적인 기상인자인 남방진동지수(SOI)와 해수면온도(SST)를 대상으로 평가해 보았다. Fourier Transform은 시간적인 특성을 파악하지 못하는 반면에 Wavelet Transform은 수문시계열이 갖는 시간적인 특성을 유지하면서 빈도에 대한 스펙트럼을 보다 효율적으로 평가할 수 있었다. Wavelet Transform을 이용하여 분석한 결과 국내 월강수량은 1년을 중심으로 강한 스펙트럼을 나타내고 있으며 연강수량은 2-8년 주기에서 통계적으로 유의한 주기를 확인할 수 있었다. SOI와 SST에서는 2-8년 주기가 지배적임을 확인할 수 있었다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Wind and Structures
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• 제3권3호
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• pp.159-175
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• 2000

Many practical time series, including pressure signals measured on roof-corners of low-rise buildings in quartering winds, consist of relatively quiescent periods interrupted by intermittent transients. The dyadic wavelet transform is used to detect these transients in pressure time series and a relatively simple pattern classification scheme is used to detect underlying structure in these transients. Statistical analysis of the resulting pattern classes yields a library of signal "building blocks", which are useful for detailed characterization of transients inherent to the signals being analyzed.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• 한국전산구조공학회논문집
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• 제24권4호
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• pp.355-364
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• 2011

본 논문은 이미지 처리나 신호처리 및 정보압축 등에 사용되는 웨이블릿 급수를 이용하여 4계 타원형 미분방정식을 풀때 그 방법에 대하여 논의하고자 한다. 본 논문에서 사용한 Hat 웨이블릿 함수는 $H^1$-공간에 속한 급수로서 일반적으로 2계 타원형 미분방정식에 적용하기에는 무리가 없으나 4계 타원형 미분방정식에 적용하기에는 불충분한 미분가능회수를 가지고 있다. 따라서 이 문제를 극복하기 위해 모멘트와 처짐을 미지수로 하는 선형방정식을 순차적으로 구성하고 풀어내는 방법을 사용하였다. 모멘트와 처짐을 미지수로 하는 순차적 해석법은 탄성하중법(모멘트면적법)의 응용으로 생각할 수 있다. 또한 그 정식화과정에서 무요소법과 동일한 점과 차이점을 언급하였다. 예측한 바와 같이 Hat 웨이블릿 함수의 항을 많이 고려할수록 수치해석의 해가 향상되는 것을 확인할 수 있었다. 또한 응력특이를 갖는 오일러보 문제의 경우 제안된 해석법은 종래의 유한요소 해석값과도 비교되었다.