• The KIPS Transactions:PartD
• /
• v.13D no.4 s.107
• /
• pp.513-524
• /
• 2006

Normalization transform is very useful for finding the overall trend of the time-series data since it enables finding sequences with similar fluctuation patterns. The previous subsequence matching method with normalization transform, however, would incur index overhead both in storage space and in update maintenance since it should build multiple indexes for supporting arbitrary length of query sequences. To solve this problem, we propose a single index approach for the normalization transformed subsequence matching that supports arbitrary length of query sequences. For the single index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. The inclusion-normalization transform normalizes a window by using the mean and the standard deviation of a subsequence that includes the window. Next, we formally prove correctness of the proposed method that uses the inclusion-normalization transform for the normalization transformed subsequence matching. We then propose subsequence matching and index building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to $2.5{\sim}2.8$ times over the previous method. Our approach has an additional advantage of being generalized to support many sorts of other transforms as well as normalization transform. Therefore, we believe our work will be widely used in many sorts of transform-based subsequence matching methods.

• The Korean Journal of Financial Management
• /
• v.26 no.1
• /
• pp.65-94
• /
• 2009

The event of change in KOSPI 200 Index composition is one of the main subjects for the test of EMH. According to EMH, when a certain event is not related with firm's fundamental value, stock price should not change after the announcement of news. This hypothesis leads us to the conclusion of horizontal demand curve of stock. This logic was questioned by Shleifer(1986) and argued that downward sloping demand curve hypothesis was supported. But Harris and Gruel(1986) found a different empirical evidence that price reversal occurs in the long run, which is called price pressure hypothesis. They argued that short term price effect by large block trading (price pressure) is offset in the long run because these event is unrelated to fundamental value. Therefor, they argued that EMH can not be rejected in the long run. Until now, there are two empirical studies with Korean market data in this area. Using a data with same time period of $1996{\sim}1999$, Kweon and Park(2000) and Ahn and Park(2005) showed that stock price or beta is not significantly affected by change in index composition. This study retested this event expanding sample period from 1996 to 2006, and analyzed why this event was considered an uninformative events in the preceding studies. We analyzed a market impact by separating samples according to firm size and market condition. In case of newly enlisted firm, we found the evidence supporting price pressure hypothesis on average. However, we found the long run price effect in the sample of large firms under bearish markets. At the same time, we know that the number of samples under the category of large firms under bearish markets is relatively small, which drives the same result of supporting the hypothesis that change in index composition is a non-informative event on average. Also, the long run price effect of large size firms under bearish markets was supported by the analyses using trading volumes. On the other hand, in case of delisting from the index, we found the long run price effect but that was not supported by trading volume analyses.

• Journal of KIISE:Databases
• /
• v.28 no.3
• /
• pp.357-372
• /
• 2001

In this paper, we present the concept of generalization in constructing windows for subsequence matching and propose a new subsequence matching method. GeneralMatch, based on the generalization. The earlier work of Faloutsos et al.(FRM in short) causes a lot of false alarms due to lack of the point-filtering effect. DualMatch, which has been proposed by the authors, improves performance significantly over FRM by exploiting the point filtering effect, but it has the problem of having a smaller maximum window size (half that FRM) given the minimum query length. GeneralMatch, an improvement of DualMatch, offers advantages of both methods: it can use large windows like FRM and, at the same time, can exploit the point-filtering effect like DualMatch. GeneralMatch divides data sequences into J-sliding windows (generalized sliding windows) and the query sequence into J-disjoint windows (generalized disjoint windows). We formally prove that our GeneralMatch is correct, i.e., it incurs no false dismissal. We also prove that, given the minimum query length, there is a maximum bound of the window size to guarantee correctness of GeneralMatch. We then propose a method of determining the value of J that minimizes the number of page accesses, Experimental results for real stock data show that, for low selectivities ($10^{-6}~10^{-4}$), GeneralMatch improves performance by 114% over DualMatch and by 998% iver FRM on the average; for high selectivities ($10^{-6}~10^{-4}$), by 46% over DualMatch and by 65% over FRM on the average.

• The KIPS Transactions:PartD
• /
• v.9D no.4
• /
• pp.555-560
• /
• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• Journal of KIISE:Databases
• /
• v.33 no.2
• /
• pp.201-213
• /
• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• Journal of KIISE:Databases
• /
• v.28 no.4
• /
• pp.643-654
• /
• 2001

This paper discusses an effective processing of similarity search that supports time warping in large sequence database. Time warping enables finding sequences with similar patterns even when they are of different length, Previous methods fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. They have to scan all the database, thus suffer from serious performance degradation in large database. Another method that hires the suffix tree also shows poor performance due to the large tree size. In this paper we propose a new novel method for similarity search that supports time warping Our primary goal is to innovate on search performance in large database without false dismissal. to attain this goal ,we devise a new distance function $D_{tw-Ib}$ consistently underestimates the time warping distance and also satisfies the triangular inequality, $D_{tw-Ib}$ uses a 4-tuple feature vector extracted from each sequence and is invariant to time warping, For efficient processing, we employ a distance function, We prove that our method does not incur false dismissal. To verify the superiority of our method, we perform extensive experiments . The results reveal that our method achieves significant speedup up to 43 times with real-world S&P 500 stock data and up to 720 times with very large synthetic data.

• Journal of Energy Engineering
• /
• v.27 no.4
• /
• pp.36-49
• /
• 2018

This study analyzed the public preference and acceptance regarding renewable energy projects through Choice Based Conjoint Analysis. The results show that the surveyed respondents consider the leading authority of the projects, as the most important factor when considering participating in renewable energy initiatives. Following this, the mode of participation and profit distribution and the power plant location are also viewed as important, whereas participation through decision making regarding the projects was less important. Also when participating in renewable energy projects, respondents tend to prefer to financially participating through loans or owning shares rather than volunteering support for the business such as sharing information, stating one's views, or providing cooperation and coordination. Therefore, the focus is on distributional justice, such as financial investment and profit distribution, rather than procedural justice, for instance decision making. When analyzing the part-worths utilities for the participation attribute, the respondents most preferred to receiving dividends based on earnings by owning shares with the local government in charge of the entire projects. As a consequence, the results suggest that it is important to have local government get involved and have trust-worthy governing systems in place for the initiation of the public participating-renewable energy projects.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.1
• /
• pp.183-191
• /
• 2022

The Korea Stewardship Code 'Principles on the Fiduciary Responsibilities of Institutional Investors' was introduced in 2016 and the National Pension Service adopted it in 2018. the National Pension Service casted 'dessent' vote on the agenda which is able to reduce the ownership interest of shareholder in general meeting. This paper examines whether 'dissent' voting affected on the ownership interest of shareholder or not. The 'dissent' vote on the agenda are related to revision artical of corperation, appointment or compensation of director and auditor, approval of financial statements ect. The proxies of earnings management is discretionary accruals calculated by modified Jones model. The control variablies are size of assets, liabilities per assets, returns on assets. The results of this study are as followings. First, the 'dissent' voting on the agenda are related to revision artical of corperation, M&A, approval of financial statements ect. are not significant because their sample size is too small, Second, the 'dissent' voting on appointment of director and auditor affected on reduction of discretionary accruals. So the National Pension Service activism shall affect on increasing the ownership interest of shareholder. Third, the 'dissent' voting on compensation of director and auditor is not affected on reduction of discretionary accruals. This results show that 'unconditional dissent voting' on the agenda in general meeting is not to reduce the ownership interest of shareholder.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.17-26
• /
• 2023

In this paper, we propose a several smoothing techniques are compared and applied to increase the application of the LSTM-based learning model and its effectiveness. The applied smoothing technique is Savitky-Golay, exponential smoothing, and weighted moving average. Through this study, the LSTM algorithm with the Savitky-Golay filter applied in the preprocessing process showed significant best results in prediction performance than the result value shown when applying the LSTM model to Bitcoin data. To confirm the predictive performance results, the learning loss rate and verification loss rate according to the Savitzky-Golay LSTM model were compared with the case of LSTM used to remove complex factors from Bitcoin price prediction, and experimented with an average value of 20 times to increase its reliability. As a result, values of (3.0556, 0.00005) and (1.4659, 0.00002) could be obtained. As a result, since crypto-currencies such as Bitcoin have more volatility than stocks, noise was removed by applying the Savitzky-Golay in the data preprocessing process, and the data after preprocessing were obtained the most-significant to increase the Bitcoin prediction rate through LSTM neural network learning.

• Journal of Korean Society of Disaster and Security
• /
• v.16 no.1
• /
• pp.1-11
• /
• 2023

The comprehensive natural disaster reduction plan, the highest plan in the disaster prevention field, was implemented by local governments. second plan is currently being formulated. In order to minimize human and property damage, structural and non-structural measures for each of the nine disaster types are established and implemented for 10 years. Structural measures are based on engineering and quantitative analysis, and the criteria for setting reduction measures are clear. Non-structural measures, however, currently lack the set criteria. the basic disaster and safety management law included the safety vulnerable class in 2018. Currently, the safety vulnerable class of the detailed establishment criteria of the comprehensive natural disaster reduction plan is being established, including children, the elderly, and the disabled. However, due to the lack of data securing and database construction by local governments, it is difficult to prepare a location map for establishing reduction measures for the safety vulnerable. Therefore, in this study, OPEN API data of the safety vulnerable class were collected and statistical information and GIS of SGIS information services were used. The distribution diagram of the safety vulnerable class in Samcheok, Gangwon-do, which is a sample area, and the distribution diagram of the safety vulnerable class in units of the output area (OA) in Geundeok-myeon were prepared.