Complexity from ordinal pattern positioned slopes (COPPS)

Measuring complexity allows to characterize complex systems. Existing techniques are limited to simultaneously measure complexity from short length data sets, detect transitions and periodic dynamics. This paper presents an approach based on ordinal pattern positioned slopes (OPPS). It considers exclusively OPPS group occurrences to compute the complexity from OPPS (COPPS) as the average number of patterns and applies to short data series. The COPPS measure was successfully applied to simulation data for measuring complexity, detecting transition phases and regular dynamics, distinguishing between chaotic and stochastic dynamics; and to real-world data for detecting arrhythmia ECG beats. •New data encoding combining slopes and positions to enhance complexity evaluation.•New COPPS method measuring complexity from short measurement data sets.•Distinction between chaotic and stochastic data and robustness against noise.•Self-similarities and model assumptions are not considered.•Easy detection of phase transitions and application to ECG beat classification.