정량적 연관규칙 탐색 기법을 이용한 시계열 분석 = Time Series Analysis Using Quantitative Association Rule

다국어 초록 (Multilingual Abstract)

Association Rule Mining,(Agrawal et al., 1993) is an exploration method which searches associative relations between item sets in large database. Association Rule Mining, in general, searches association between qualitative items. To adapt Association Rule Mining to quantitative attributes, Quantitative Association Rule Mining is introduced in 1996.
Sequential Pattern Mining and N-Dimensional Inter Transaction Mining are applications of Association Rule for Time series analysis by extending time dimensions to the Association Rule.
Analyzing complex time series, such as stock price movement, using quantitative model is limited, and Technical Analysis like a chart analysis is an alternative approach for them. Technical Analysis recognizes patterns, and analyzes impacts effected by the pattern.
In this work, we defined pattern on the time series syntactically and analyzed the patterns and recognized the patterns which is frequently emerges in the time series, and, explored the other patterns as an impact of discovered patterns in former step. For this purpose, we used N-dimensional Inter Transaction Association Rule and syntactically described pattern(of both quantitative, qualitative attribute). So it enables pattern recognition, analyzing impacts and forecasting of complex time series such as stock prive movement

목차 (Table of Contents)

• 목차
• Ⅰ. 서론
• Ⅱ. 관련 연구
• Ⅲ. 문제 정의
• Ⅳ. 시계열 분석
• Ⅴ. 결론 및 향후 과제