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Supporting the Page-Hinkley Test with Empirical Mode Decomposition for Change Detection

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

In the dynamic scenarios faced nowadays, when handling non stationary data streams it is of utmost importance to perform change detection tests. In this work, we propose the Intrinsic Page Hinkley Test (iPHT), which enhances the Page Hinkley Test (PHT) eliminating the user-defined parameter (the allowed magnitude of change of the data that are not considered real distribution change of the data stream) by using the second order intrinsic mode function (IMF) which is a data dependent value reflecting the intrinsic data variation. In such way, the PHT change detection method is expected to be more robust and require less tunes. Furthermore, we extend the proposed iPHT to a blockwise approach. Computing the IMF over sliding windows, which is shown to be more responsive to changes and suitable for online settings. The iPHT is evaluated using artificial and real data, outperforming the PHT.

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Notes

  1. 1.

    available at http://www.mathworks.com/matlabcentral/fileexchange/19681-hilbert-huang-transform (accessed in March 17th 2016).

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Acknowledgments

This work was supported by the Portuguese Science Foundation (FCT) through national funds, and co-funded by the FEDER, within the PT2020 Partnership Agreement and COMPETE2020 under projects IEETA (UID/CEC/00127/2013) and VR2market (funded by the CMU Portugal program, CMUP-ERI/FIA/0031/2013). Raquel Sebastião acknowledges her Post-Doc grant (BPD/UI62/6777/2015).

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Authors and Affiliations

  1. Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193, Aveiro, Portugal

    Raquel Sebastião & José Maria Fernandes

Authors
  1. Raquel Sebastião
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  2. José Maria Fernandes
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Corresponding author

Correspondence to Raquel Sebastião .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. RaÅ›

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Sebastião, R., Fernandes, J.M. (2017). Supporting the Page-Hinkley Test with Empirical Mode Decomposition for Change Detection. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_48

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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