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Advances in Self-Organizing Maps and Learning Vector Quantization pp 107–117Cite as

Segment Growing Neural Gas for Nonlinear Time Series Analysis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

Abstract

In this work we propose an extension to Growing Neural Gas (GNG) for dealing with the spatiotemporal quantization of time series. The two main changes to the original GNG algorithm are the following. First, the basic unit of the GNG network is changed from a node to a linear segment joining two nodes. Secondly, temporal connections between neighboring units in time are added. The proposed algorithm called Segment GNG (SGNG) is compared with the original GNG and Merge GNG algorithms using three benchmark time series: Rössler, Mackey-Glass and \(\text {NH}_{3}\) Laser. The algorithms are applied to the quantization of trajectories in the state space representation of these time series. The results show that the SGNG outperforms both GNG and Merge GNG in terms of quantization error and temporal quantization error.

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Notes

  1. 1.

    Available at http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe.html.

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Acknowledgments

This research was supported by Conicyt-Chile under grants Fondecyt 1140816, Conicyt DPI20140090 and by the Ministry of Economy Development and Tourism of Chile under grant IC12089 awarded to the Millennium Institute of Astrophysics.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Chile, Santiago, Chile

    Jorge R. Vergara, Pablo A. Estévez & Álvaro Serrano

  2. Millennium Institute of Astrophysics, Santiago, Chile

    Jorge R. Vergara & Pablo A. Estévez

  3. Advanced Mining Technology Center, Santiago, Chile

    Pablo A. Estévez

Authors
  1. Jorge R. Vergara
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  2. Pablo A. Estévez
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  3. Álvaro Serrano
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Corresponding author

Correspondence to Pablo A. Estévez .

Editor information

Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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Vergara, J.R., Estévez, P.A., Serrano, Á. (2016). Segment Growing Neural Gas for Nonlinear Time Series Analysis. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

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