Zunino, LucianoOlivares, FelipeFernández Bariviera, AurelioRosso, Osvaldo A.2019-09-192019-09-192017-030375-9601http://ri.itba.edu.ar/handle/123456789/1762"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. "enANALISIS DE SERIES DE TIEMPOPROCESOS ESTACIONARIOSCLASIFICACIONArtículos de Publicaciones Periódicas