• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.110-112
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• 2000

최근 몇 년간 시계열 데이터의 저장 및 분석에 대한 연구가 활발히 진행되고 있으며, 시계열 데이터베이스에서 유사패턴(similarity pattern)을 탐색하는 기법이 광범위한 응용분야에서 중요한 연구주제로 자리잡고 있다. 본 논문에서는 회귀분석방법을 바탕으로 한 분해 시계열 방법을 이용함으로써 기존의 유사성의 개념을 확장시켰다. 즉, 시계열 데이터가 가지고 있는 패턴을 여러 성분으로 분해하여 각기 다른 저장 공간에 저장하고, 이를 이용하여 유사성을 탐색할 때에도 분리된 각 성분 중 특정 변동특성이 유사한 데이터를 추가적으로 요구되는 시간없이 검색할 수 있다. 이는 전체 시계열 데이터를 이해하는데 뿐만 아니라 데이터를 예측하는 방법에도 유용하게 사용될 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.81-86
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• 2022

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.579-599
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• 2014

The purpose of this study was to identify the trends and risk of bias of research using time series analysis on health and welfare in Korea and to suggest a direction for future health and welfare research. The database searches identified 6,543 papers. Following the process for screening and selecting, a total of 91 papers were included in the systematic review. There has been a steady increase in the number of articles using time series analysis from 1987 to 2013. Time series analysis was applied in medicine and health science journals. The main goals were explanation and description. Most of the subjects were heath status and utilization of healthcare services. The main model used in the time series analysis was ARIMA followed by time series regression. The data were gathered from various sources, including the national statistical office and government agencies. For assessing risk of bias, some studies were found to have inadequate sample sizes or showed no time series graphs and plots. These findings suggest greater widespread utilization of time series analysis in the field of health and welfare and to use the appropriate analysis methods and statistical procedures to obtain more reliable results to improve the quality of research.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.295-306
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• 2016

The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.41-75
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• 2018

The purpose of this study is to analyze the characteristics of Korean media on the topic of archives & records management based on time-series analysis. In this study, from January, 1999 to June, 2018, 4,680 news articles on archives & records management topics were extracted from BigKinds. In order to examine the characteristics of the media coverage on the archives & records management topic, this study was analyzed to the difference of the press coverage by period, subject, and type of the media. In addition, this study was conducted word-frequency based content analysis and semantic network analysis to investigate the content characteristics of media on the subject. Based on these results, this study was analyzed to the differences of media coverage by period, subject, and type of media. As a result, the news in the field of records management showed that there was a difference in the amount of news coverage and news contents by period, subject, and type of media. The amount of news coverage began to increase after the Presidential Records Management Act was enacted in 2007, and the largest amount of news was reported in 2013. Daily newspapers and financial newspapers reported the largest amount of news. As a result of analyzing news reports, during the first 10 years after 1999, news topics were formed around the issues arising from the application and diffusion process of the concept of archives & records management. However, since the enactment of the Presidential Records Management Act, archives & records management has become a major factor in political and social issues, and a large amount of political and social news has been reported.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.5-22
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• 2021

This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.

• Tunnel and Underground Space
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• v.25 no.4
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• pp.303-319
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• 2015

Text mining is one of the branches of data mining and is used to find any meaningful information from the large amount of text. In this study, we analyzed titles and keywords of two SCI journals on rock engineering by using text mining to find major research area, trend and associations of research fields. Visualization of the results was also included for the intuitive understanding of the results. Two journals showed similar research fields but different patterns in the associations among research fields. IJRMMS showed simple network, that is one big group based on the keyword 'rock' with a few small groups. On the other hand, RMRE showed a complex network among various medium groups. Trend analysis by clustering and linear regression of keyword - year frequency matrix provided that most of the keywords increased in number as time goes by except a few descending keywords.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.105-110
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• 2009

현재 NASA에서 제공되는 MODIS 지표반사율자료(MOD09)는 MODIS영상을 이용한 각종 주제자료들의 중요한 입력 자료로 사용되고 있으며, MODIS 지표반사율 자료에 대한 객관적인 검증연구가 필요한 실정이다. 따라서 본 연구에서는 MOD09의 검증관련 초기 연구로서, 남한에 분포하는 불변성 타겟(invariant target)을 대상으로 2006년 일별 250m MODIS 지표반사율자료(MOD09GQK)자료의 객관적 검증을 시도하였다. 우선, MOD09 QA(Quality Assurance)자료를 이용하여 구름의 영향을 받은 화소를 제거한 후, 수치지도와 토지피복도를 이용하여 정의한 불변성 타겟에 해당되는 MOD09영상의 화소값을 추출하였다. 이와 같이 추출된 시계열 MOD09GHK영상의 화소값에 1차 회귀분석을 적용하여 이상 반사율 값을 탐지하고, 그 원인을 분석하였다. 검증 결과 나지지역에 대해서 0.0186의 RMSE값이 나타났으며, 인공물의 경우 0.2891의 RMSE값을 보였다. 발생된 이상 화소를 살펴보면, 구름, 그림자, 눈에 영향에 의해 발생한 것도 있으며, 원인을 알 수 없는 이상 화소들도 분포하였다. 향후 연구에서는 한반도 전역의 MODIS 시계열 반사율영상을 대상으로 MODIS 대기보정알고리즘과 입력인자의 적합성을 판단하기 위한 연구를 진행할 예정이다.