• 인터넷정보학회논문지
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• 제25권2호
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• pp.39-47
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• 2024

시계열 분석은 시간 순서로 정렬된 데이터로부터 다양한 정보와 인사이트를 발견하기 위한 방법으로 많은 조직에서 비즈니스 문제 해결을 위해 적용하고 있다. 그중에서 시계열 유사도 측정은 패턴이 비슷한 시계열들을 식별하기 위한 단계로서 시계열 검색 및 군집화와 같은 시계열 분석 응용에서 매우 중요하다. 본 연구에서는 전체 시계열이 아닌 이상치들을 중심으로 시계열 유사도 측정을 계산 효율적으로 수행하는 방법을 제안한다. 이와 관련하여 이상탐지를 통해 추출된 서브시퀀스 집합에 대한 유사도 측정 결과와 시계열 전체에 대한 유사도 측정 결과 사이의 순위 상관관계를 측정 및 분석하여 제안 방법을 검증한다. 실험 결과로써, 주식 종목 시계열 데이터에 이상치 비율 10% 을 적용한 유사도 측정으로부터 최대 0.9 이상의 스피어만 순위 상관계수를 확인하였다. 결론적으로 제안 방법을 통해 시계열 유사도 측정에 소요되는 계산량을 유의미하게 절감하는 동시에 신뢰 가능한 시계열 검색 및 군집화 결과를 기대할 수 있다.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 춘계학술대회
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• pp.91-101
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• 2004

The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• Communications for Statistical Applications and Methods
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• 제21권6호
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• pp.471-486
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• 2014

This study introduces a new type of symbolic data, a candle chart-valued time series. We aggregate four stock indices (i.e., open, close, highest and lowest) as a one data point to summarize a huge amount of data. In other words, we consider a candle chart, which is constructed by open, close, highest and lowest stock indices, as a type of symbolic data for a long period. The proposed candle chart-valued time series effectively summarize and visualize a huge data set of stock indices to easily understand a change in stock indices. We also propose novel approaches for the candle chart-valued time series modeling based on a combination of two midpoints and two half ranges between the highest and the lowest indices, and between the open and the close indices. Furthermore, we propose three types of sum of square for estimation of the candle chart valued-time series model. The proposed methods take into account of information from not only ordinary data, but also from interval of object, and thus can effectively perform for time series modeling (e.g., forecasting future stock index). To evaluate the proposed methods, we describe real data analysis consisting of the stock market indices of five major Asian countries'. We can see thorough the results that the proposed approaches outperform for forecasting future stock indices compared with classical data analysis.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• 한국기계가공학회지
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• 제5권3호
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• pp.36-42
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• 2006

The analytical realization of uncoupled system was introduced in this study using times series and its spectrum analysis. The ARMAX spectra of time series methods were compared with the conventional FFT spectrum. Also, the response of second order system uncoupled was solved using the Runge-Kutta Gill method. In this numerical analysis, the displacement, velocity and acceleration were calculated. The displacement response among them was used for the power spectrum analysis. The ARMAX algorithm in time series was proved to be appropriate for the mode estimation and spectrum analysis. Using the separate response of first and second mode, each modes were calculated separately and the response of mixed modes was also analyzed for the mode estimation using several time series methods.

• 한국국방경영분석학회지
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• 제37권1호
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• pp.39-48
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• 2011

야전에 저장된 탄약의 수명을 예측하는 것은 군의 전투지원 핵심요소로 실무적으로 매우 중요한 의미가 있다. 본 연구는 6년간 수행한 155mm 추진장약(KD541)의 ASRP(Ammunition Stockpile Reliability Program : 저장탄약신뢰성평가) 결과를 기초로 추진장약 추진제의 안정제함량 변화에 따른 시계열분석 (ARIMA 모델) 방법론을 적용 저장탄약수명을 예측하였다. 이번 연구는 기존의 회귀분석 모델을 활용한 연구방법과 다르게 시계열분석을 적용하되 미니 탭 프로그램을 활용하여 시계열분석을 적용 저장탄약수명을 예측하였다. 이러한 분석결과 155mm 추진장약(KD541) 저장수명은 35~43년으로 예측되었다.

• 한국경영과학회지
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• 제38권2호
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• pp.65-73
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• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• 센서학회지
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• 제15권6호
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• pp.433-441
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• 2006

Because of the low power and low rate of a sensor network, outlier is frequently occurred in the time series data of sensor network. In this paper, we suggest periodic pattern analysis that is applied to the time series data of sensor network and predict outlier that exist in the time series data of sensor network. A periodic pattern is minimum period of time in which trend of values in data is appeared continuous and repeated. In this paper, a quantization and smoothing is applied to the time series data in order to analyze the periodic pattern and the fluctuation of each adjacent value in the smoothed data is measured to be modified to a simple data. Then, the periodic pattern is abstracted from the modified simple data, and the time series data is restructured according to the periods to produce periodic pattern data. In the experiment, the machine learning is applied to the periodic pattern data to predict outlier to see the results. The characteristics of analysis of the periodic pattern in this paper is not analyzing the periods according to the size of value of data but to analyze time periods according to the fluctuation of the value of data. Therefore analysis of periodic pattern is robust to outlier. Also it is possible to express values of time attribute as values in time period by restructuring the time series data into periodic pattern. Thus, it is possible to use time attribute even in the general machine learning algorithm in which the time series data is not possible to be learned.