Reliable Estimation of Minimum Embedding Dimension Through Statistical Analysis of Nearest Neighbors

[+] Author and Article Information
David Chelidze

Nonlinear Dynamics Laboratory Department of Mechanical, Industrial and Systems Engineering University of Rhode Island, Kingston, RI 02881, USA

1Corresponding author.

ASME doi:10.1115/1.4036814 History: Received October 10, 2016; Revised May 04, 2017


False nearest neighbors (FNN) is one of the essential methods used in estimating the minimally sufficient embedding dimension in delay-coordinate embedding of deterministic time series. Its use for stochastic and noisy deterministic time series is problematic and erroneously indicates a finite embedding dimension. Various modifications to the original method have been proposed to mitigate this problem, but those are still not reliable for noisy time series. Here, nearest-neighbor statistics are studied for uncorrelated random time series and contrasted with the corresponding deterministic and stochastic statistics. New composite FNN metrics are constructed and their performance is evaluated for deterministic, stochastic, and random time series. In addition, noise-contaminated deterministic data analysis shows that these composite FNN metrics are robust to noise. All FNN results are also contrasted with surrogate data analysis to show their robustness. The new metrics clearly identify random time series as not having a finite embedding dimension and provide information about the deterministic part of stochastic processes. These metrics can also be used to differentiate between chaotic and random time series.

Copyright (c) 2017 by ASME
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