Abstract
We consider mixing chaotic time series and present two prediction methods for such systems. The methods we develop are the Nearest Neighbors method and the Radial Basis Functions method. We discuss the optimal prediction horizon according to the sampling time step. We also discuss a reliable method measuring the prediction error. We illustrate our results with simulations.
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Guégan, D., Mercier, L. (1998). Non-Parametric Forecasting Techniques for Mixing Chaotic Time Series. In: Procházka, A., Uhlíř, J., Rayner, P.W.J., Kingsbury, N.G. (eds) Signal Analysis and Prediction. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-1768-8_25
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DOI: https://doi.org/10.1007/978-1-4612-1768-8_25
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