• 경영과학
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• 제8권1호
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• pp.3-11
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• 1991

Non-Stationary transition probabilities models which is incorporated into a Markov framework with exogenous variables to account for some of variability are discussed, and extended for alternative procedure. Also as an application of the methodology, the size change of aggregate time-series data on the planted system of agricultural crops is estimated, and evaluated for the precision of time-varying evolution statistically.

• 응용통계연구
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• 제34권4호
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• pp.611-622
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• 2021

본 논문에서는 변동성의 비대칭성과 비정상성을 동시에 고려하고 있다. 다양한 변동성 모형을 분석하고 있으며 모수적-붓스트랩을 통한 예측분포를 이용하여 변동성 모형의 예측 성능을 비교하고 있다. 오차항 분포로서 표준정규분포 및 표준화 t-분포를 고려하였으며 1-시차 후 예측과 2-시차 후 예측을 미국의 다우지수 사례를 통해 설명하였다.

• Journal of Mechanical Science and Technology
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• 제18권9호
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• pp.1565-1571
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• 2004

Ride characteristics of a vehicle moving on a rough ground with changing travel velocity are analyzed in this paper. The solution is difficult due to the non-stationary characteristics of the problem. Hence a new technique has been proposed to overcome this difficulty. This new technique is employed in the analysis of ride characteristics of a vehicle with changing velocity in the time/frequency domain. It is found that the proposed technique gives successful results in modelling non-stationary responses in terms of a series of stationary responses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.666-668
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• 2000

In this paper, a fuzzy prediction method is proposed for time series data having uncertainty and non-stationary characteristics. Conventional methods, which use past data directly in prediction procedure, cannot properly handle non-stationary data whose long-term mean is floating. To cope with this problem, a data preprocessing technique utilizing the differences of original time series data is suggested. The difference sets are established from data. And the optimal difference set is selected for input of fuzzy predictor. The proposed method based the Takigi-Sugeno-Kang(TSK or TS) fuzzy rule. Computer simulations show improved results for various time series.

• 한국인터넷방송통신학회논문지
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• 제17권2호
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• pp.179-190
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• 2017

Hand Accelerometer는 인간신체 운동 패턴을 실시간으로 파악하는데 널리 사용되고 있다. 그러므로 행동 유형을 정확하게 파악하는 것은 아주 중요하다. 이 과정에서 각 행동유형의 형태를 미리 정확하게 파악하는 것이 중요하다. 인간의 신체 행동은 센서를 통해 수집된 시계열 데이터로 표현된다. 이 데이터는 비안정적, 비선형적 성격을 가지고 있다. 그래서 이런 성격의 데이터의 유형을 효율적으로 추출하는 방법을 찾는 것은 매우 중요하다. 힐버트-황 변환은 비안정적 비선형적 요소를 시계열데이터에서 효율적으로 추출하는 방법이다. 이 방법을 위의 시계열 데이터에 적용한 결과 핵심패턴이 성공적으로 추출되었다.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• 천문학회보
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• 제41권1호
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• pp.81.2-81.2
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• 2016

Many signals in the nature have power-law behaviors, namely they are "scale-free". The method of detrended fluctuation analysis (DFA), as one of the popular methods (e.g., Rescaled range analysis and Spectral analysis) for determining scale-free nature of time series, has a very important advantage that the DFA can be applied to both stationary and non-stationary signals. The analysis of time series using the DFA has been broadly used in physiology, finance, hydrology, meteorology, geology, and so on. We performed the DFA of 16 Spaceweather HMI Active Region Patch (SHARP) parameters for 38 HMI Active Region Patches (HARPs) obtained by Solar Dynamics Observatory (SDO) from May 2010 to June 2014. The main results from this study are as follows. (1) The most of the time series data are non-stationary. (2) The DFA scaling exponents of "mean vertical current density" for 38 HARPs have a negative correlation coefficient (-0.41) with flare index. (3) The DFA scaling exponents of parameters such as "Sum of the absolute value of net currents per polarity", "Absolute value of the net current helicity", and "Mean photospheric excess magnetic energy density" for the most active HARPs having more than 10 major flares, have positive correlation coefficients (0.64, 0.59, and 0.53, respectively) with the ratio of "the number of CMEs associated with major flares" to "the number of major flares". Physical interpretations on our results will be discussed.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.502-507
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• 2003

Toward the development of practical methods for observed data oriented bispectral estimation, an automatic means for determining the smoothing bandwidth of bispectral windows is proposed, that can also provide an associated optimum bispectral estimate of stationary non-Gaussian signals, systematically only from an observed time series datum of finite length. For the conventional non-parametric bispectral estimation, the MSE (mean squared error) of the normalized estimate is reviewed under a certain mixing condition and sufficient data length, mainly from the viewpoint of the inverse relation between its bias and variance with respect to the smoothing bandwidth. Based on the fundamental relation, a systematic method not only for determining the bandwidth, but also for obtaining the optimum bispectral estimate is presented by newly introducing a MSE evaluation index of the estimate only from an observed time series datum of finite length. The effectiveness and fundamental features of the proposed method are illustrated by the basic results of numerical experiments.

• 한국지반환경공학회 논문집
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• 제12권4호
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• pp.53-63
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• 2011

동적 지진해석 수행 시 적절한 입력지진파를 선정 생성하는 것은 매우 중요하다. 현재 국내에서는 일반적으로 국외에서 계측된 강진 기록이나 인공지진파가 입력지진파로 사용된다. 계측지진기록은 지진파의 고유성질인 시간에 따라서 주파수 특성이 변이하는 비정상(Non-Stationary) 특성을 가지고 있지만 설계 응답스펙트럼과는 일치하지 않으며 주파수영역에서 생성된 인공지진파는 설계 응답스펙트럼과는 일치하지만 정상(Stationary) 특성을 가지고 있는 단점이 있다. 본 연구에서는 계측기록의 Non-stationary 특성을 보존하되 동시에 설계 응답스펙트럼에 상응하는 지진파를 생성하였다. 적용된 기법은 Impulse 함수를 이용하여 시간영역에서 지진기록을 목표 스펙트럼에 상응하도록 보정하는 알고리즘이다. 적용 결과, 시간영역 변화 알고리즘은 성공적으로 계측 지진기록을 설계 응답스펙트럼와 일치하도록 조정할 수 있으며 원 지진기록의 Non-stationary 특성을 보존하는 것으로 나타났다. 나아가 계측 지진기록과 보정된 지진기록을 적용한 비선형 지반응답해석을 수행한 결과, 보정된 지진파를 이용한 결과가 보다 합리적인 것으로 나타났다. 본 연구에서 변환된 지진기록은 기존 기록의 문제점을 보완하는 진보된 입력지진파인 것으로 나타났으며 추후 지진해석 시 이를 준용하는 것이 합리적일 것으로 판단된다.