Journal
PHYSICS LETTERS A
Volume 381, Issue 11, Pages 1021-1028Publisher
ELSEVIER
DOI: 10.1016/j.physleta.2017.01.047
Keywords
Time series analysis; Abbe value; Turning points; Financial data; Electroencephalogram data; Heart rate variability
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Funding
- Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Argentina
- Pontificia Universidad Catolica de Valparaiso
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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. (C) 2017 Elsevier B.V. All rights reserved.
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