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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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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