Feature Dependencies

(Analysis credit: Brendan Harris using the catch22 Julia package).

Although the selection framework used to generate the catch22 feature set included a step to reduce redundancy, it was not designed to generate an independent set of features.

Here we see an example of this, showing the Spearman correlation coefficient between all pairs of features, quantifying the similarity of their outputs across a diverse range of 1000 empirical time series:

• We find a large cluster of features sensitive to the autocorrelation of a time series.
• We also find a small cluster of two highly correlated features, DN_HistogramMode_5 and DN_HistogramMode_10, which measure the mode of the z-scored time-series distribution using different numbers of bins.

This dependency structure should be taken in mind when interpreting the results of catch22 analyses.