Discover Motifs in Multi Dimensional Time-Series Using the
Principal Component Analysis and the MDL Principle
Recently, the detection of a previously unknown, frequently occurring pattern
has been regarded as a difficult problem. Such pattern is clled as ``motif''.Many
researchers have proposed algorithms for discovering the motif.However, if the
optimal period length of the motif is not known in advance,we can not use these
algorithms for discovering the motif.In our research, we attempt to dynamically
determine the optimum period length
using the MDL principle.Moreover, in order to apply this algorithm to the multi
dimensional time-series, we transform the time-series into one dimensional time-series
by using the Principal Component Analysis.
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