• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3112-3127
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• 2018

Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.

• 한국콘텐츠학회논문지
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• 제10권9호
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• pp.97-106
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• 2010

시계열 데이터를 효율적이고 효과적으로 처리하기 위해 다양한 시계열 표현 방법들이 제안되었다. SAX(Symbolic Aggregate approXimation)는 단편화와 이산화 기법들을 결합한 시계열 표현 방법으로, 시계열 분류 문제에 성공적으로 적용되었다. 그러나 SAX는 시계열의 움직임을 평활하여 시계열의 중요한 동적 패턴들을 정확히 표현하기 위해 세그먼트 수를 크게 해야 한다. 본 논문은 PIPs (Perceptually Important Points)탐지 기법과 Persist 이산화 방법을 결합한 시계열 표현 방법을 제안한다. 제안된 방법은 시계열의 중요한 변곡점들을 나타내는 PIP 들을 탐지하여 고차원 시계열의 동적 움직임을 저차원 공간에서 표현한다. 그리고 시계열의 자기 전이와 주변 확률 분포를 KL 다이버전스에 적용하여 최적의 이산화 영역들을 결정한다. 제안된 방법은 시계열의 차원 축소과정에서 정보 손실을 최소화하여 시계열 분류의 성능을 향상시킨다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.85-88
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• 2021

제조 설비에서 짧은 주기로 수집된 제조 데이터는 시간 간격이 일정하지 않은 불규칙 샘플링 시계열이고 값이 불안정하여 큰 분산을 가지는 경우가 많다. 본 논문에서는 단순이동평균법을 이용하여 불규칙 시계열의 시간 간격을 일정하게 보정함과 동시에 값의 분산을 줄이는 보정 기법을 제안하고, 제안된 보정 기법이 생산 설비의 이상탐지의 성능 향상에 효과가 있음을 확인하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권5호
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• pp.233-240
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• 2001

This paper presents a time series prediction method using a fuzzy rule-based system. Extracting fuzzy rules by performing a simple one-pass operation on the training data is quite attractive because it is easy to understand, verify, and extend. The simplest method is probably to relate an estimate, x(n+k), with past data such as x(n), x(n-1), ..x(n-m), where k and m are prefixed positive integers. The relation is represented by fuzzy if-then rules, where the past data stand for premise part and the predicted value for consequence part. However, a serious problem of the method is that it cannot handle nonstationary data whose long-term mean is varying. To cope with this, a new training method is proposed, which utilizes the difference of consecutive data in a time series. In this paper, typical previous works relating time series prediction are briefly surveyed and a new method is proposed to overcome the difficulty of prediction nonstationary data. Finally, computer simulations are illustrated to show the improved results for various time series.

• 한국태양에너지학회 논문집
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• 제37권5호
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• pp.77-84
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• 2017

The purpose of this paper is to propose the way of computing conduction time series factors (CTSF) using numerical method. After the accuracy of the numerical solution procedure being verified, the method is applied to the wall type 24 and roof type 14 of ASHARE to find the conduction time series coefficients, so called conduction time series factors. The results agree well with the values presented in the ASHRAE handbook. The method proposed can be easily applied to find unknown CTSF for more complex structures. It provides information about the temperature changes at a given location and time, thus validity of generated CTSF can be checked easily.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.338-341
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• 2003

Recently, the chaotic method is employed to forecast a near future of uncertain phenomena. This method makes it possible by restructuring an attractor of given time-series data in multi-dimensional space through Takens' embedding theory. However, many economical time-series data are not sufficiently chaotic. In other words, it is hard to forecast the future trend of such economical data on the basis of chaotic theory. In this paper, time-series data are divided into wave components using wavelet transform. It is shown that some divided components of time-series data show much more chaotic in the sense of correlation dimension than the original time-series data. The highly chaotic nature of the divided component enables us to precisely forecast the value or the movement of the time-series data in near future. The up and down movement of TOPICS value is shown so highly predicted by this method as 70％.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.265-274
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• 2003

Time series data are influenced by the external events such as holiday, strike, oil shock, and political change, so the external events cause a sudden change to the time series data. We regard the observation as outlier that occurred as a result of external events. In general, it is called intervention if we know the period and the reason of external events, and it makes an analyst difficult to establish a time series model. Therefore, it is important that we analyze the styles and effects of intervention. In this paper, we considered the linear time series model with invention and compared with nonlinear time series models such as ARCH, GARCH model and also we compared with the combination prediction method that Tong(1990) introduced. In the practical case study, we compared prediction power with RMSE among linear, nonlinear time series model with intervention and combination prediction method.

• 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.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.851-876
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• 2018

Dynamic time warping (DTW) is the main algorithms for time series alignment. However, it is unsuitable for quasi-periodic time series. In the current situation, except the recently published the shape exchange algorithm (SEA) method and its derivatives, no other technique is able to handle alignment of this type of very complex time series. In this work, we propose a novel algorithm that combines the advantages of the SEA and the DTW methods. Our main contribution consists in the elevation of the DTW power of alignment from the lowest level (Class A, non-periodic time series) to the highest level (Class C, multiple-periods time series containing different number of periods each), according to the recent classification of time series alignment methods proposed by Boucheham (Int J Mach Learn Cybern, vol. 4, no. 5, pp. 537-550, 2013). The new method (quasi-periodic dynamic time warping [QP-DTW]) was compared to both SEA and DTW methods on electrocardiogram (ECG) time series, selected from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) public database and from the PTB Diagnostic ECG Database. Results show that the proposed algorithm is more effective than DTW and SEA in terms of alignment accuracy on both qualitative and quantitative levels. Therefore, QP-DTW would potentially be more suitable for many applications related to time series (e.g., data mining, pattern recognition, search/retrieval, motif discovery, classification, etc.).

• 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.