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Conceptual Pattern Matching of Time Series Data using Hidden Markov Model

은닉 마코프 모델을 이용한 시계열 데이터의 의미기반 패턴 매칭

  • 조영희 (단국대학교 전자계산학과) ;
  • 전진호 (단국대학교 전자계산학과) ;
  • 이계성 (단국대학교 컴퓨터과학부)
  • Published : 2008.05.31

Abstract

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.

Keywords

Pattern Matching;Hidden Markov Model;Time Series

References

  1. G. Xianping, "Pattern Matching in Financial Time series Data," 1998.
  2. X. Ge and P. Smyth, "Deformable Markov model templates for time-series pattern matching," In proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, MA, Vol.20, No,23, pp.81-90, 2000(8). https://doi.org/10.1145/347090.347109
  3. E. Keogh, "A fast and robust method for pattern matching in time-series databases," In Proc. of the 9th Int. Conf. on Tools with Artificial Intelligence, pp.578-584, 1997.
  4. E. Keogh and P. Smyth, "A probabilistic Approach to fast pattern matching in time series databases," In the third conference on Knowledge discovery in Database and Data mining, pp.24-30, 1997.
  5. W. Wang and J. Yang, P. S. Yu, "Mining Patterns in Long Sequential Data with noise," ACM SIGKDD Exploration, pp.28-33, 2001. https://doi.org/10.1145/380995.381008
  6. J. Hamaker and J. Zhao, "Bayesian Information criterion for automatic model selection," Technical Report, Mississippi State University, 1999(5).
  7. M. Azzouzi, I. T. Nabney, "Analysing time series structure with Hidden Markov Models," in Proceeding of Neural Network for Signal Processing VIII, pp.402-408, 1998. https://doi.org/10.1109/NNSP.1998.710670
  8. S. Singh, "Pattern Modelling in time-series forecasting," Cybernetics and Systems-An International Journal, Vol.31, issue 1, 2000. https://doi.org/10.1080/019697200124919
  9. A. Malegaonkar, A. Ariyaeeinia, P. Sivakumaran, and J. Fortuna, "Unsupervised Speaker Change Detection Using Probabilistic Pattern Matching," IEEE Signal Processing Letters, Vol.13, No.8, 2006(8). https://doi.org/10.1109/LSP.2006.873656
  10. A. Panuccio, M. Bicego, and V. Murino, "A Hidden Markov Model-based approach to sequential data clustering," In T. Caelli, A. Amin, R. Duin, M. Kamel, and D.D. Ridder, : Structural, Syntactic and Statistical Pattern Recognition. LNCS 2396, pp.734-742, 2002. https://doi.org/10.1007/3-540-70659-3_77
  11. 전호상, 남궁재찬, “혼합된 GA-BP 알고리즘을 이용한 얼굴 인식 연구”, 한국정보처리학회 논문지, 제7권, 제2호, 2000.
  12. 전진호, 조영희, 이계성, “주가 운동양태 예측을 위한 모델 결정에 과한 연구”, 한국컨텐츠학회논문지, 제6권, 제6호, 2006.