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Weighted moving averaging revisited: an algebraic approach

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Abstract

An algebraic approach for the selection of weight coefficients for weighted moving averaging is proposed in this paper. The algebraic complexity of the sequence transformed by weighted moving averaging is set as a target criterion for the optimization problem of weight coefficients. A special computational setup is constructed in order to tackle the inevitable additive noise for real-world time series. Computational experiments prove that the proposed approach can outperform time series predictors based on classical moving averaging.

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Acknowledgments

This work was supported by the Lithuanian Science Council under project No. MIP-078/2015.

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Authors and Affiliations

  1. Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-147, Kaunas, 51368, Lithuania

    Mantas Landauskas & Minvydas Ragulskis

  2. Department of Applied Mathematics, Kaunas University of Technology, Studentu 50-325, Kaunas, 51368, Lithuania

    Zenonas Navickas

  3. Sport Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania

    Alfonsas Vainoras

Authors
  1. Mantas Landauskas
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  2. Zenonas Navickas
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  3. Alfonsas Vainoras
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  4. Minvydas Ragulskis
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Corresponding author

Correspondence to Mantas Landauskas.

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Communicated by Antonio José Silva Neto.

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Landauskas, M., Navickas, Z., Vainoras, A. et al. Weighted moving averaging revisited: an algebraic approach. Comp. Appl. Math. 36, 1545–1558 (2017). https://doi.org/10.1007/s40314-016-0309-9

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  • DOI: https://doi.org/10.1007/s40314-016-0309-9

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