• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.8
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• pp.944-949
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• 2007

The profiles of on-line sensors such as DO, ORP and pH can provide useful information about pollutant removal reaction in sequencing batch reactor. For detection of denitrification completion, the nitrate hee point from ORP profile has been considered as a main indicator of denitrification completion. However, many researchers pointed out that the nitrate knee usually disappeared been the progress of denitrification is so fast and it makes the fault at detection of denitrification completion. In this paper, dynamic time warping(DTW) method and discriminant analysis were used to detect and isolate the profiles of two cases, denitrification completed and uncompleted. As the results, proposed methods can detect state of denitrification successfully.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.2
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• pp.102-108
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• 2009

DO, ORP and pH values measured during SBR operation can provide information about removal reaction of organic contaminants and nutrient materials in the reactor. It is already generalized control strategy to control reaction phase time using their special patterns indicating the end of the removal reactions. However, those informations are limited to point out the end time of oxidative reaction in the aerobic phase or reductive reaction in the anoxic phase without giving quantitative value of influent loading level. In this research, a diagnosis algorithm which can estimate the loading level of carbon and ammonia as high, medium and low was developed using the basic measurements like DO, ORP, and pH. It will be possible to know the level of influent loading rate from those online measurements without experimental analysis.