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Hybridization of Analytic Programming and Differential Evolution for Time Series Prediction

  • Roman SenkerikEmail author
  • Adam Viktorin
  • Michal Pluhacek
  • Tomas Kadavy
  • Ivan Zelinka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

Abstract

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the applicability and differences in performance of AP driven by basic canonical strategy of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). An experiment with three case studies has been carried out here with the several time series consisting of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

Keywords

Analytic programming Differential evolution SHADE Time series prediction 

Notes

Acknowledgements

This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant SGS 2017/134 of VSB-Technical University of Ostrava; and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/Cebia-Tech/2017/004.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Roman Senkerik
    • 1
    Email author
  • Adam Viktorin
    • 1
  • Michal Pluhacek
    • 1
  • Tomas Kadavy
    • 1
  • Ivan Zelinka
    • 2
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  2. 2.Faculty of Electrical Engineering and Computer ScienceTechnical University of OstravaOstrava-PorubaCzech Republic

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