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tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation

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Advanced Analytics and Learning on Temporal Data (AALTD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14343))

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Abstract

Segmentation of multivariate time series data is a valuable technique for identifying meaningful patterns or changes in the time series that can signal a shift in the system’s behavior. We introduce a domain agnostic framework ‘tGLAD’ for multivariate time series segmentation using conditional independence (CI) graphs that capture the partial correlations. It draws a parallel between the CI graph nodes and the variables of the time series. Consider applying a graph recovery model uGLAD to a short interval of the time series, it will result in a CI graph that shows partial correlations among the variables. We extend this idea to the entire time series by utilizing a sliding window to create a batch of time intervals and then run a single uGLAD model in multitask learning mode to recover all the CI graphs simultaneously. As a result, we obtain a corresponding temporal CI graphs representation of the multivariate time series. We then designed a first-order and second-order based trajectory tracking algorithm to study the evolution of these graphs across distinct intervals. Finally, an ‘Allocation’ algorithm is designed to determine a suitable segmentation of the temporal graph sequence which corresponds to the original multivariate time series. tGLAD provides a competitive time complexity of O(N) for settings where number of variables \(D<<N\). We demonstrate successful empirical results on a Physical Activity Monitoring data. (Software: https://github.com/Harshs27/tGLAD).

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Authors and Affiliations

  1. Microsoft Research, Redmond, USA

    Shima Imani & Harsh Shrivastava

Authors
  1. Shima Imani
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  2. Harsh Shrivastava
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Corresponding author

Correspondence to Harsh Shrivastava .

Editor information

Editors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Georgiana Ifrim

  2. University of Rennes 2, Rennes, France

    Romain Tavenard

  3. University of Southampton, Southampton, UK

    Anthony Bagnall

  4. Humboldt University of Berlin, Berlin, Germany

    Patrick Schaefer

  5. University of Rennes, Rennes, France

    Simon Malinowski

  6. Claude Bernard University Lyon 1, Villeurbanne, France

    Thomas Guyet

  7. Orange Innovation, Lannion, France

    Vincent Lemaire

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Imani, S., Shrivastava, H. (2023). tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation. In: Ifrim, G., et al. Advanced Analytics and Learning on Temporal Data. AALTD 2023. Lecture Notes in Computer Science(), vol 14343. Springer, Cham. https://doi.org/10.1007/978-3-031-49896-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-49896-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49895-4

  • Online ISBN: 978-3-031-49896-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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