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Algebraic Level-Set Approach for the Segmentation of Financial Time Series

  • Rita Palivonaite
  • Kristina Lukoseviciute
  • Minvydas Ragulskis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

Adaptive algebraic level-set segmentation algorithm of financial time series is presented in this paper. The proposed algorithm is based on the algebraic one step-forward predictor with internal smoothing, which is used to identify a near optimal algebraic model. Particle swarm optimization algorithm is exploited for the detection of a base algebraic fragment of the time series. A combinatorial algorithm is used to detect intervals where predictions are lower than a predefined level. Moreover, the combinatorial algorithm does assess the simplicity of the identified near optimal algebraic model. Automatic adaptive identification of quasi-stationary segments can be employed for complex financial time series.

Keywords

Segmentation Financial time series Particle swarm optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Rita Palivonaite
    • 1
  • Kristina Lukoseviciute
    • 1
  • Minvydas Ragulskis
    • 1
  1. 1.Research Group for Mathematical and Numerical Analysis of Dynamical SystemsKaunas University of TechnologyKaunasLithuania

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