An Efficient Method for Discovering Motifs in Streaming Time Series Data

  • Cao Duy TruongEmail author
  • Duong Tuan Anh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 244)


The discovery of repeated subsequences, time series motifs, is a problem which has great utility for several higher-level data mining tasks, including classification, clustering, forecasting and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static time series data. However, for many applications, the streaming nature of time series demands a new kind of methods for discovery of time series motifs. In this paper, we develop a new method for motif discovery in streaming time series. In this method we use significant extreme points to determine motif candidates and then cluster motif candidates by BIRCH algorithm. The method is very effective not only for large time series data but also for streaming environment since it needs only one-pass of scan through the whole data.


Time Series Extreme Point Time Series Data Static Time Series Cluster Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringHo Chi Minh City University of TechnologyHo Chi MinhVietnam

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