• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.25-32
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• 2004

In this study, we use various change-point detection methods to detects Korea economic crisis of 1997, and then compares their performance. In change-point detection method, there are three major categories: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. Through the application to Korea foreign exchange rate during her economic crisis, we compare the employed change-point detection methods and, furthermore, determine which of them performs better.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.585-592
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• 2004

This study detects Korea economic crisis of 1997 using various change-point detection methods and then compares their performance. In change-point detection method, there are three major categories: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. Through the application to Korea foreign exchange rate during her economic crisis, we compare the employed change-point detection methods and, furthermore, determine which of them performs better.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• Asia pacific journal of information systems
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• v.11 no.4
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• pp.99-111
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• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1091-1098
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• 2006

Park et al.(2005) and Choi et al.(2006) studied quick detection of variance change point for time series data in progress. For efficient detection they used moving variance ratio equipped with two tuning parameters; information tuning parameter p and lag tuning parameter q. In this paper, the moving variance ratio is studied under harsh conditions.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.427-436
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• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.515-523
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• 2000

In this paper we detect edges using stutistical methods of the change-point problem. For this, we perform the hypothesis testing for differences in gray levels to see whether any $n\timesn$ subimage contains edge segments. The proposed method based on the twosample Kolmogorov-Smirnov test is introduced and the likelihood ratio test and the \VolfeSchechtman test for change-point problem arc also applied for edge detection. \Ve perform the experimental study to assess the performance of these methods in both noisy and uncontaminated sample noises.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.127-150
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• 2022

We discuss multiple change-point estimation as edge detection in piecewise smooth functions with finitely many jump discontinuities. In this paper we propose change-point estimators using concentration kernels with Fourier coefficients. The change-points can be located via the signal based on Fourier transformation system. This method yields location and amplitude of the change-points with refinement via concentration kernels. We prove that, in an appropriate asymptotic framework, this method provides consistent estimators of change-points with an almost optimal rate. In a simulation study the proposed change-point estimators are compared and discussed. Applications of the proposed methods are provided with Nile flow data and daily won-dollar exchange rate data.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.355-365
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• 2000

The interferometric correlation, or coherence, is calculated to measure the variance of the interferometric phase and amplitude within the neighbourhood of any location within the image at a result of SAR (Synthetic Aperture Radar) interferometric process which utilizes the phase information of the images. The coherence contains additional information that is useful for detecting point targets which change their location in an area of interest (AOI). In this research, a RGB colour composite image was generated with a intensity image (master image), a intensity change image as a difference between master image and slave image, and a coherence image generated as a part of SAR interferometric processing. We developed a technique performing detection of a point target movement using SAR interferometry and applied it to suitable tandem pair images of ERS-1 and ERS-2 as test data. The possibility of change detection of a point target in the AOI could be identified with the technique proposed in this research.