artículo de publicación periódica.page.titleprefix A simple and fast representation space for classifying complex time series
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Date
2017-03
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Abstract
"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. "
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Keywords
ANALISIS DE SERIES DE TIEMPO, PROCESOS ESTACIONARIOS, CLASIFICACION