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Time-Series Data Prediction using Hidden Markov Model and Similarity Search for CRM

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2009, 14(5), pp.19-28
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

조영희 1 전진호 1 Gye Sung Lee 1

1단국대학교

Accredited

ABSTRACT

Prediction problem of the time-series data has been a research issue for a long time among many researchers and a number of methods have been proposed in the literatures. In this paper, a method is proposed that similarities among time-series data are examined by use of Hidden Markov Model and Likelihood and future direction of the data movement is determined. Query sequence is modeled by Hidden Markov Modeling and then the model is examined over the pre-recorded time-series to find the subsequence which has the greatest similarity between the model and the extracted subsequence. The similarity is evaluated by likelihood. When the best subsequence is chosen, the next portion of the subsequence is used to predict the next phase of the data movement. A number of experiments with different parameters have been conducted to confirm the validity of the method. We used KOSPI to verify suggested method.

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