  The non-stationary evolution of observable quantities in complex systems can
frequently be described as a juxtaposition of quasi-stationary spells. Given
that standard theoretical and data analysis approaches usually rely on the
assumption of stationarity, it is important to detect in real time series
intervals holding that property. With that aim, we introduce a segmentation
algorithm based on a fully non-parametric approach. We illustrate its
applicability through the analysis of real time series presenting diverse
degrees of non-stationarity, thus showing that this segmentation procedure
generalizes and allows to uncover features unresolved by previous proposals
based on the discrepancy of low order statistical moments only.
