We present a data-driven approach created to explore the full information of data sets, avoiding parametric assumptions. The evaluations are based on Information Theory concepts, introducing an objective measure of information and uncertainty. The approach was applied to automatically identify rainfall-runoff events in discharge time series, however it is generic enough to be adapted to other practical applications.