  We present an adaptation of the standard Grassberger-Proccacia (GP) algorithm
for estimating the Correlation Dimension of a time series in a non subjective
manner. The validity and accuracy of this approach is tested using different
types of time series, such as, those from standard chaotic systems, pure white
and colored noise and chaotic systems added with noise. The effectiveness of
the scheme in analysing noisy time series, particularly those involving colored
noise, is investigated. An interesting result we have obtained is that, for the
same percentage of noise addition, data with colored noise is more
distinguishable from the corresponding surrogates, than data with white noise.
As examples for real life applications, analysis of data from an astrophysical
X-ray object and human brain EEG, are presented.
