• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.357-368
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• 2006

This paper discusses the way of processing time-series subsequence matching under time warping. Time warping enables finding sequences with similar patterns even when they are of different lengths. The prefix-querying method is the first index-based approach that performs time-series subsequence matching under time warping without false dismissals. This method employs the $L_{\infty}$ as a base distance function for allowing users to issue queries conveniently. In this paper, we extend the prefix-querying method for absorbing $L_1$, which is the most-widely used as a base distance function in time-series subsequence matching under time warping, instead of $L_{\infty}$. We also formally prove that the proposed method does not incur any false dismissals in the subsequence matching. To show the superiority of our method, we conduct performance evaluation via a variety of experiments. The results reveal that our method achieves significant performance improvement in orders of magnitude compared with previous methods.

• Journal of the Korean Professional Engineers Association
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• v.2 no.8
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• pp.29-32
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• 1969

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.579-586
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• 2021

Time series forecasting refers to predicting future time information based on past time information. Accurately predicting future information is crucial because it is used for establishing strategies or making policy decisions in various fields. Recently, a transformer model has been mainly studied for a time series prediction model. However, the existing transformer model has a limitation in that it has an auto-regressive structure in which the output result is input again when the prediction sequence is output. This limitation causes a problem in that accuracy is lowered when predicting a distant time point. This paper proposes a sequential decoding model focusing on the style transformation technique to handle these problems and make more precise time series forecasting. The proposed model has a structure in which the contents of past data are extracted from the transformer-encoder and reflected in the style-based decoder to generate the predictive sequence. Unlike the decoder structure of the conventional auto-regressive transformer, this structure has the advantage of being able to more accurately predict information from a distant view because the prediction sequence is output all at once. As a result of conducting a prediction experiment with various time series datasets with different data characteristics, it was shown that the model presented in this paper has better prediction accuracy than other existing time series prediction models.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1025-1034
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• 2015

Traditional Shewhart control charts assume that the observations are independent over time. Current progress in measurement and data collection technology lead to the presence of autocorrelated process data that may affect poor performance in statistical process control. One of the most popular charts for autocorrelated data is to model a correlative structure with an appropriate time series model and apply control chart to the sequence of residuals. Model parameters are estimated by an in-control Phase I reference sample since they are usually unknown in practice. This paper deals with the effects of parameter estimation on Phase II control limits to monitor autocorrelated data.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.210-216
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• 2015

It is well known that the structure of brain and consciousness of human have a phenomena of complex system. The human emotion have a many kind. The love is one of human emotion, which have been studied in sociology and psychology as a matter of great interested thing. In this paper, we consider a same and different time delay in love equation of Romeo and Juliet. We represent a behavior of love as a time series and phase portrait, and analyze the difference of behaviors between a same and different time delay.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.63.1-63.1
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• 2016

일성정시의는 표준시보장치인 보루각루의 시각을 교정하는 천문시계로 알려져 있다. "세종실록"에 기록된 김돈(金墩,1385~1440)의 일성정시의(日星定時儀)의 서(序)와 명(銘)에는 기기의 상세한 구조와 치수, 그리고 사용법을 소개하고 있다. 우리는 실록의 기록을 분석하여 세 종류의 일성정시의로 구분하였다. 또한 Needham et al. (1986)의 연구와 비교하여 일구백각환, 성구백각환, 주천도분환, 계형 등의 사용법을 분석하였다. 우리는 이러한 분석을 통해 해시계와 별시계로써의 일성정시의의 시간 측정 정밀도를 제시하였다.

• Journal of the Korean Wood Science and Technology
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• v.20 no.4
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• pp.31-42
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• 1992

최근에 서로 다른 생장추세를 나타내고 있는 남(南)애리조나산(産) 침엽수 4수종(樹種)(Pseudotsuga menziesii, Pinus ponderosa, Pinus strobiformis, Abies concolor)의 연륜폭 연대기(年代記)로 부터 반응함수를 구하기 위하여, 표준화 방법을 면밀히 검토하였으며 시계열모델에 의한 사전(事前) 여과화(濾過化)(prewhitening)로 전생장량(前生長量)을 배제하는 것이 효과적인 것인지도 조사하였다. 전통적으로 사용되던 지수(指數) 또는 질선(直線)방정식에 의한 표준화가 대부분 성공적으로 적용되었으나 최근생장이 급격히 감소된 경우는 스플라인함수를 이용하는 것이 효과적이었다. 계절(季節)모델이 적용된 Pinus ponderosa의 경우를 제외하곤 사전여과화 전후(前後)의 반응함수간(間)에 뚜렷한 차이를 발견할 수 없었다.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.217-224
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• 2003

The volatility in the financial data is usually measured by conditional variance. Two main streams for gauging conditional variance are stochastic volatility (SV) model and autoregressive type approach (GARCH). This article is conducting comparative study between SV and GARCH through the Korean Stock Prices Index (KOSPI) data. It is seen that SV model is slightly better than GARCH(1,1) in analyzing KOSPI data.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.2
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• pp.75-84
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• 1994

기계가공프로세스에서 절삭공구의 마멸과 채터진동은 공작기계의 가동율과 생산성을 크게 저해하는 요인이 되고 있다. 본 연구에서는 공구마멸과 채터현상이 혼재하는 상황에서, 이들 두 현상을 동시에 검출하는데, AE 및 가속도센서에서 검출된 신호와 AR계수 및 주파수대역 평균에너지를 특징입력으로 하는 인공신경회로망을 이용하였다. 그 결과, 공구마멸과 채터현상에 대응하는 서로 다른 신호특징의 차이를 동시에 식별하는 데 인공신경 회로망의 유용성을 입증하였으며, 시계열모델의 AR계수(70 .approx. 90%)보다는 주파수대역에너지법의 평균에너지 (80 .approx. 100%)를 신경회로망의 특징입력으로 하는 경우가 높은 성공률을 나타내었다.