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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing

3PGCIC 2017: Advances on P2P, Parallel, Grid, Cloud and Internet Computing pp 787–795Cite as

Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 13)

Abstract

Adaptive predictive models can use conventional and nonconventional neural networks for highly non-stationary time series prediction. However, conventional neural networks present a series of known drawbacks. This paper presents a brief discussion about this concern as well as how the basis of higher-order neural units can overcome some of them; it also describes a sliding window technique alongside the batch optimization technique for capturing the dynamics of non-stationary time series over a Quadratic Neural Unit, a special case of higher-order neural units. Finally, an experimental analysis is presented to demonstrate the effectiveness of the proposed approach.

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Acknowledgements

This project is supported by Research Grant No. DSA/103.5/16/10473 awarded by PRODEP and the Autonomous University of Ciudad Juarez. Title - Detection of Cardiac Arrhythmia Patterns through Adaptive Analysis.

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Authors and Affiliations

  1. Autonomous University of Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico

    Ricardo Rodríguez Jorge, Edgar Martínez García & Rafael Torres Córdoba

  2. Opole University, Opole, Poland

    Jolanta Mizera-Pietraszko

  3. Czech Technical University in Prague, Prague, Czech Republic

    Jiri Bila

Authors
  1. Ricardo Rodríguez Jorge
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  2. Edgar Martínez García
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  3. Jolanta Mizera-Pietraszko
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  4. Jiri Bila
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  5. Rafael Torres Córdoba
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Corresponding author

Correspondence to Ricardo Rodríguez Jorge .

Editor information

Editors and Affiliations

  1. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  2. Open University of Catalonia, Barcelona, Spain

    Prof. Santi Caballé

  3. Fukuoka Institute of Technology (FIT), Fukuoka, Japan

    Leonard Barolli

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Rodríguez Jorge, R., Martínez García, E., Mizera-Pietraszko, J., Bila, J., Torres Córdoba, R. (2018). Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_74

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  • DOI: https://doi.org/10.1007/978-3-319-69835-9_74

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