EFFECT OF NOISE ON THE AVERAGED FALSE NEIGHBORS METHOD APPLIED TO SIMULATED AND EXPERIMENTAL CHAOTIC TIMES SERIES

We examine the influence of noise on the averaged false neighbors method (AFN), which was proposed by L. Cao to identify the deterministic nature and estimate the minimum embedding dimension of time series. In a numerical experiment, we add a raising amount of noise to time series from numerical simulation of well known chaotic models and to experimentally recorded time series. We find that the AFN method acknowledges the deterministic nature of the underlying process of these noisy times series. For the assessment of the embedding, sensitivity to noise generally decreases when the dimension of the underlying dynamical process increased.