Efficient use of correlation entropy for analysing time series data

Abstract

The correlation dimension D2 and correlation entropy K2 are both important quantifiers in nonlinear time series analysis. However, use of D2 has been more common compared to K2 as a discriminating measure. One reason for this is that D2 is a static measure and can be easily evaluated from a time series. However, in many cases, especially those involving coloured noise, K2 is regarded as a more useful measure. Here we present an efficient algorithmic scheme to compute K2 directly from a time series data and show that K2 can be used as a more effective measure compared to D2 for analysing practical time series involving coloured noise.