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A generalized matrix profile framework with support for contextual series analysis

Dieter De Paepe (UGent) , Sander Vanden Hautte (UGent) , Bram Steenwinckel (UGent) , Filip De Turck (UGent) , Femke Ongenae (UGent) , Olivier Janssens (UGent) and Sofie Van Hoecke (UGent)
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Abstract
The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile.
Keywords
TIME-SERIES, DISCOVERY, Time series, Anomaly detection, Matrix Profile, Distance matrix, Series, Distance Matrix, Contextual Matrix Profile

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MLA
De Paepe, Dieter, et al. “A Generalized Matrix Profile Framework with Support for Contextual Series Analysis.” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 90, 2020.
APA
De Paepe, D., Vanden Hautte, S., Steenwinckel, B., De Turck, F., Ongenae, F., Janssens, O., & Van Hoecke, S. (2020). A generalized matrix profile framework with support for contextual series analysis. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 90.
Chicago author-date
De Paepe, Dieter, Sander Vanden Hautte, Bram Steenwinckel, Filip De Turck, Femke Ongenae, Olivier Janssens, and Sofie Van Hoecke. 2020. “A Generalized Matrix Profile Framework with Support for Contextual Series Analysis.” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 90.
Chicago author-date (all authors)
De Paepe, Dieter, Sander Vanden Hautte, Bram Steenwinckel, Filip De Turck, Femke Ongenae, Olivier Janssens, and Sofie Van Hoecke. 2020. “A Generalized Matrix Profile Framework with Support for Contextual Series Analysis.” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 90.
Vancouver
1.
De Paepe D, Vanden Hautte S, Steenwinckel B, De Turck F, Ongenae F, Janssens O, et al. A generalized matrix profile framework with support for contextual series analysis. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. 2020;90.
IEEE
[1]
D. De Paepe et al., “A generalized matrix profile framework with support for contextual series analysis,” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 90, 2020.
@article{8660952,
  abstract     = {The Matrix Profile is a state-of-the-art time series analysis technique that can be used for motif discovery, anomaly detection, segmentation and others, in various domains such as healthcare, robotics, and audio. Where recent techniques use the Matrix Profile as a preprocessing or modeling step, we believe there is unexplored potential in generalizing the approach. We derived a framework that focuses on the implicit distance matrix calculation. We present this framework as the Series Distance Matrix (SDM). In this framework, distance measures (SDM-generators) and distance processors (SDM-consumers) can be freely combined, allowing for more flexibility and easier experimentation. In SDM, the Matrix Profile is but one specific configuration. We also introduce the Contextual Matrix Profile (CMP) as a new SDM-consumer capable of discovering repeating patterns. The CMP provides intuitive visualizations for data analysis and can find anomalies that are not discords. We demonstrate this using two real world cases. The CMP is the first of a wide variety of new techniques for series analysis that fits within SDM and can complement the Matrix Profile.},
  articleno    = {103487},
  author       = {De Paepe, Dieter and Vanden Hautte, Sander and Steenwinckel, Bram and De Turck, Filip and Ongenae, Femke and Janssens, Olivier and Van Hoecke, Sofie},
  issn         = {0952-1976},
  journal      = {ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE},
  keywords     = {TIME-SERIES,DISCOVERY,Time series,Anomaly detection,Matrix Profile,Distance matrix,Series,Distance Matrix,Contextual Matrix Profile},
  language     = {eng},
  pages        = {12},
  title        = {A generalized matrix profile framework with support for contextual series analysis},
  url          = {http://dx.doi.org/10.1016/j.engappai.2020.103487},
  volume       = {90},
  year         = {2020},
}

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