• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.539-540
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• 2022

본 논문에서는 정확한 장기 시계열 예측을 위해 시계열 데이터의 다양한 스케일 (시간 규모)에서 표현을 학습하는 트랜스포머 모델을 제안한다. 제안하는 모델은 시계열의 다중 스케일 특징을 추출하고, 이를 트랜스포머에 반영하여 예측 시계열을 생성하는 구조로 되어 있다. 스케일 정규화 과정을 통해 시계열의 전역적 및 지역적인 시간 정보를 효율적으로 융합하여 종속성을 학습한다. 3 가지의 다변량 시계열 데이터를 이용한 실험을 통해 제안하는 방법의 우수성을 보인다.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2001.11a
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• pp.25-30
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• 2001

엔진이나 콤프레셔 등과 같은 기기 진동의 동특성은 불규칙적으로 변동하는 성분을 갖는 랜덤 프로세스로 그것을 수학적으로 명확히 기술하는 것은 어렵다/sup 1)/. 하지만 통계학적인 입장에서 시계열 데이터를 보면 시계열 데이터가 확률변수로서 각각의 모집단에 속한다. 따라서 이 점에 주목하여 시계열의 확률적인 특징을 추출하는 것으로, 각각의 시계열 데이터를 확률공간으로 구별하는 것이 가능하다면 시계열 데이터에 의해 표현되어진 상태의 식별가능하다는 이론이 성립된다/sup 2)/.(중략)

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.50-58
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• 1998

EEG 생리신호의 분석은_ 국내에서도 최근에 활발한 연구가 진행되고 있으나, 시계열을 이용한 분석법은 많은 전문적인 지식을 요구하고 있기 때문에 시계열을 전문적으로 연구하지 않은 사람들에게는 많은 어려움을 내포하고 있다. 그러므로 시계열분석에 대한 지식이 혀는 분석자라도 보다 쉽게 이해하고 분석이 가능한 모형구축 및 판별분류에 대한 신호분석용 시계열분신 Tool의 개발이 미진한 상태이기 때문에 시계열분석에 의한 뇌파 신호의 분류에 대한 시스템을 개발하였다.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.631-638
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• 2002

Stochastic model has been widely used for the forecasting of time series. However, this study tries to perform the precipitation forecasting by fuzzy time series model using fuzzy concept. The published fuzzy based models are used for the forecasting of time series and also we suggest that the combination of fuzzy time series models and neuro-fuzzy system can increase the forecastibility of the models. The precipitation time series in illinois, USA is analyzed for the forecasting by the known fuzzy time series models and the suggested methodology in this study. As a result, we know that the suggested methodology shows more exact results than the known models.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.219-227
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• 2007

The time series models have been used to analyze and predict the network traffic. In this paper, we compare the performance of the time series models for prediction of network traffic. The feasibility study showed that a class of nonlinear time series models can be outperformed than the linear time series models to predict the network traffic.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2968-2970
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• 2000

시계열(time series)이란 한 사상 또는 여러 사상에 대하여 시간의 흐름에 따라 일정한 간격으로 이들을 관측하여 기록한 자료를 말한다. 이러한 시계열은 어떠한 경제현상이나 자연현상에 관한 시간적 변화를 나타내는 역사적 계열(historical series)이므로 어느 한 시점에서 관측된 시계열자료는 그 이전까지의 자료들에 주로 의존하게 된다. 따라서 시계열분석을 통한 예측에서는 과거의 자료들을 분석하여 법칙성을 발견해서 이를 모형화하여 추정하고. 이 추정된 모형을 사용하여 미래에 관측될 값들을 예측하게 된다. 본 연구에서는 ARMA (p, q)모형 (autoregressive moving-average model)을 이용하여 시계열 데이터를 분석하며 계수의 추정에는 Levinson-Durbin 알고리듬과 Newton-Raphson Method를 이용한다.

• Economic and Environmental Geology
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• v.50 no.4
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• pp.267-276
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• 2017

Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.981-993
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• 2015

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.67-79
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• 2011

The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.145-152
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• 2010

Bayesian information criterion is used to do clustering for time series data. To acquire more stable clusters, multiple seeds are chosen first for the algorithm. Once clusters being set up, most similar time series data in the cluster to the one under consideration are to be chosen for prediction test. These chosen time series data are used to extract valid Markov rules by which we test the prediction accuracy. We confirmed that clustering with multiple seeds led to better prediction performance.