Segment Growing Neural Gas for Nonlinear Time Series Analysis

  • Jorge R. Vergara
  • Pablo A. Estévez
  • Álvaro Serrano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 428)


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.



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jorge R. Vergara
    • 1
    • 2
  • Pablo A. Estévez
    • 1
    • 2
    • 3
  • Álvaro Serrano
    • 1
  1. 1.Department of Electrical EngineeringUniversity of ChileSantiagoChile
  2. 2.Millennium Institute of AstrophysicsSantiagoChile
  3. 3.Advanced Mining Technology CenterSantiagoChile

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