We propose a method for estimating the correlation dimension and correlation entropy of a time series. It is based on a generalization of the correlation integral that is specifically useful when the time series is corrupted with Gaussian measurement noise. From computational experiments we conclude that reasonable estimates for the noise level, correlation dimension, and correlation entropy can be found for time series with up to 20% noise. The method appears to be fairly robust with respect to the noise distribution.
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Cees G. H. Diks (1996) studied this question.
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