Mining Motifs in Massive Time Series Databases

  • Authors:
  • Pranav Patel;Eamonn Keogh;Jessica Lin;Stefano Lonardi

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
  • Year:
  • 2002

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Abstract

The problem of efficiently locating previously knownpatterns in a time series database (i.e., query by content) hasreceived much attention and may now largely be regardedas a solved problem. However, from a knowledge discoveryviewpoint, a more interesting problem is the enumeration ofpreviously unknown, frequently occurring patterns. We callsuch patterns "motifs", because of their close analogy totheir discrete counterparts in computation biology. Anefficient motif discovery algorithm for time series would beuseful as a tool for summarizing and visualizing massivetime series databases. In addition it could be used as asubroutine in various other data mining tasks, including thediscovery of association rules, clustering and classification.In this work we carefully motivate, then introduce, a non-trivialdefinition of time series motifs. We propose anefficient algorithm to discover them, and we demonstrate theutility and efficiency of our approach on several real worlddatasets.