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					  TIME-SERIES SEGMENTATION BY CLUSTER ANALYSIS AND HIDDEN
    MARKOV MODEL, WITH APPLICATION TO RATES OF RETURN

                          Stanley L. Sclove and Lon-Mu Liu
                            University of Illinois at Chicago


                                        OUTLINE


1. Introduction and background

       Motivation
             Lack of Normality and lack of independence in rates of return

2. Context
    labeling problems
              general definition of labeling problems
              classification
              cluster analysis
              time-series segmentation

3. Time-Series Segmentation
                    time-ordered data
                    time-series data
                    clustering
                    clustering with time as a variable
                            epochs vs. classes (states)
                    Hidden Markov Model
                            EM algorithm
                                   greedy algorithm for the E step
                                   Viterbi algorithm for the E step
                    Examples
                            GNP
                            Daily RORs

4. Conclusions and Discussion

          Discussion

          Extensions; Future Research

              ARIMA within classes
              2D image segmentation
              3D image segmentation

				
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posted:5/6/2011
language:English
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