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Soft Computing

, Volume 23, Issue 3, pp 1039–1048 | Cite as

Evolving nearest neighbor time series forecasters

  • Juan J. Flores
  • José R. Cedeño GonzálezEmail author
  • Rodrigo Lopez Farias
  • Felix Calderon
Methodologies and Application
  • 146 Downloads

Abstract

This article proposes a nearest neighbors—differential evolution (NNDE) short-term forecasting technique. The values for the parameters time delay \(\tau \), embedding dimension m, and neighborhood size \(\epsilon \), for nearest neighbors forecasting, are optimized using differential evolution. The advantages of nearest neighbors with respect to popular approaches such as ARIMA and artificial neural networks are the capability of dealing properly with nonlinear and chaotic time series. We propose an optimization scheme based on differential evolution for finding a good approximation to the optimal parameter values. Our optimized nearest neighbors method is compared with its deterministic version, demonstrating superior performance with respect to it and the classical algorithms; this comparison is performed using a set of four synthetic chaotic time series and four market stocks time series. We also tested NNDE in noisy scenarios, where deterministic methods are not capable to produce well-approximated models. NNDE outperforms the other approaches.

Keywords

Chaotic time series Forecasting Nearest neighbor algorithm Evolutionary algorithms 

Notes

Acknowledgements

José R. Cedeño’s doctoral program has been funded by CONACYT Scholarship No. 516226/290379.

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Juan J. Flores
    • 1
  • José R. Cedeño González
    • 1
    Email author
  • Rodrigo Lopez Farias
    • 2
  • Felix Calderon
    • 1
  1. 1.Universidad Michoacana de San Nicolás de HidalgoMoreliaMéxico
  2. 2.Centro en Investigación y Geografía y Geomática, Ing. Jorge L. Tamayo, A.C.México CityMéxico

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