• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2011년도 학술발표회
• /
• pp.90-90
• /
• 2011

Long-term nonstationary oscillations (NSOs) are commonly observed in hydrological and climatological data series such as low-frequency climate oscillation indices and precipitation dataset. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD.

• Industrial Engineering and Management Systems
• /
• 제7권1호
• /
• pp.9-22
• /
• 2008

As an extension of a previous work by the authors (Song and Esogbue, 2006), a new algorithm for automated modeling of nonstationary seasonal time series is presented in this paper. Issues relative to the methodology for building automatically seasonal time series models and periodic time series models are addressed. This is achieved by inspecting the trend, estimating the seasonality, determining the orders of the model, and estimating the parameters. As in our previous work, the major instruments used in the model identification process are correlograms of the modeling errors while the least square method is used for parameter estimation. We provide numerical illustrations of the performance of the new algorithms with respect to building both seasonal time series and periodic time series models. Additionally, we consider forecasting and exercise the models on some sample time series problems found in the literature as well as real life problems drawn from the retail industry. In each instance, the models are built automatically avoiding the necessity of any human intervention.

• Communications for Statistical Applications and Methods
• /
• 제17권3호
• /
• pp.377-390
• /
• 2010

In the case of time-series analysis, it is often more convenient to rely on the frequency domain than the time domain. Spectral density is the core of the frequency-domain analysis that describes autocorrelation structures in a time-series process. Possible ways to estimate spectral density are to compute a periodogram or to average the periodogram over some frequencies with (un)equal weights. This can be an attractive tool to measure the similarity between time-series processes. We employ the metrics based on a smoothed periodogram proposed by Park and Kim (2008) for the classification of different classes of time-series processes. We consider several lag windows with unequal weights instead of a modified Daniel's window used in Park and Kim (2008). We evaluate the performance under various simulation scenarios. Simulation results reveal that the metrics used in this study split the time series into the preassigned clusters better than do the raw-periodogram based ones proposed by Caiado et al. 2006. Our metrics are applied to an economic time-series dataset.

• 한국지능시스템학회논문지
• /
• 제19권1호
• /
• pp.25-33
• /
• 2009

최근 시계열 예측에 결론부에 선형식을 갖는 TS 퍼지 모델이 많이 이용되고 있는데, 이의 예측 성능은 정상성과 같은 데이터의 특성과 밀접한 관련이 있다. 그러므로 본 논문에서는 특히 비정상 시계열 예측에 매우 효과적인 새로운 예측 기법을 제안하였다. 시계열의 패턴이나 규칙성을 잘 끌어내기 위한 데이터 전처리 과정을 도입하고 다중 모델 TS 퍼지 예측기를 구성한 뒤, 러프집합을 이용한 적응 모델 선택 기법에 의해 입력 데이터의 특성에 따라 가변적으로 적합한 예측 모델을 선택하여 시계열 예측이 수행되도록 하였다. 마지막으로 예측 오차를 감소시키기 위하여 오차 보정 메커니즘을 추가함으로써 예측 성능을 더욱 향상시켰다. 시뮬레이션을 통해 제안된 기법의 성능을 검증하였다. 제안된 기법은 예측 모델 구현과 예측 수행 과정에서 시계열 데이터의 특성들을 잘 반영할 수 있으므로 불확실성과 비정상성을 갖는 시계열의 예측에 매우 효과적으로 이용될 수 있을 것이다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2007년도 학술발표회 논문집
• /
• pp.1437-1440
• /
• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2011년도 학술발표회
• /
• pp.91-91
• /
• 2011

In the present study, the proposed EMD and NSOR models has been applied in hydrology and climate sciences. Here, we present those applications as the following: (1) to extend future scenarios of Global Surface Temperature Anomaly including long-term oscillation component; (2) to extend the future evolution of the Eastern Canada winter precipitation; (3) to apply EMD in detecting climate change.

• Communications for Statistical Applications and Methods
• /
• 제26권1호
• /
• pp.27-34
• /
• 2019

We study the effect of detrending on the coherency between two time series processes. Many economic and financial time series variables include nonstationary components; however, we analyze the two most popular cases of stochastic and deterministic trends. We analyze the asymptotic behavior of coherency under incorrect detrending, which includes the cases of first-differencing the deterministic trend process and, conversely, the time trend removal of the unit root process. A simulation study is performed to investigate the finite sample performance of the sample coherency due to incorrect detrending. Our work is expected to draw attention to the possible distortion of coherency when the series are incorrectly detrended. Further, our results can extend to various specification of trends in aggregate time series variables.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2010년도 학술발표회
• /
• pp.581-585
• /
• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• 응용통계연구
• /
• 제33권6호
• /
• pp.713-722
• /
• 2020

본 논문에서는 금융시계열의 특징인 비대칭 변동성을 연구하고 있다. 멱변환을 동시에 고려한 멱변환-비대칭 GARCH 모형을 소개하고 있다. 변동성이 비정상인 모형을 다루고 있으며 오차항으로 표준정규분포와 더불어 표준화 t-분포도 고려하여 변동성 정상/비정상 조건을 제시하고 있다. 미국 주가 시계열인 다우지수 적용사례를 예시하였다.

• 한국소음진동공학회논문집
• /
• 제25권12호
• /
• pp.888-894
• /
• 2015

A new method for the simulation of the vehicle's interior road noise is proposed in the present study. The road noise model can synthesize road noise of a vehicle for varying driving speed within a range. In the proposed method, interior road noise is considered as a stochastic time-series, and is modeled by a nonstationary parametric model via two steps. First, each interior road noise signal, obtained from constant speed driving tests performed within a range of speed, is modeled as an autoregressive model whose parameters are estimated by using a standard method. Finally, the parameters obtained for different driving speeds are interpolated based on the varying driving speed to yield a time-varying autoregressive model. To model a full band road noise, audible frequency range is divided into an octave band using a wavelet filter bank, and the road noise in each octave band is modeled.