A simple and fast representation space for classifying complex time series

L. ZUNINO; F. OLIVARES; A. F.BARIVIERA; O. A. ROSSO

PHYSICS LETTERS A

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2017 vol. 381 p. 1021 - 1028

0375-9601

In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.