An Automated Matrix Profile for Mining Consecutive Repeats in Time Series

  • Mahtab MirmomeniEmail author
  • Yousef Kowsar
  • Lars Kulik
  • James Bailey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11013)


A key application of wearable sensors is remote patient monitoring, which facilitates clinicians to observe patients non-invasively, by examining the time series of sensor readings. For analysis of such time series, a recently proposed technique is Matrix Profile (MP). While being effective for certain time series mining tasks, MP depends on a key input parameter, the length of subsequences for which to search. We demonstrate that MP’s dependency on this input parameter impacts its effectiveness for finding patterns of interest. We focus on finding consecutive repeating patterns (CRPs), which represent human activities and exercises whilst tracked using wearable sensors. We demonstrate that MP cannot detect CRPs effectively and extend it by adding a locality preserving index. Our method automates the use of MP, and reduces the need for data labeling by experts. We demonstrate our algorithm’s effectiveness in detecting regions of CRPs through a number of real and synthetic datasets.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mahtab Mirmomeni
    • 1
    Email author
  • Yousef Kowsar
    • 1
    • 2
  • Lars Kulik
    • 1
  • James Bailey
    • 1
  1. 1.The University of MelbourneMelbourneAustralia
  2. 2.Microsoft Research Centre for Social NUIMelbourneAustralia

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