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Type: Artigo de periódico
Title: TIDES-a new descriptor for time series oscillation behavior
Author: Mariote, LE
Medeiros, CB
Torres, RD
Bueno, LM
Abstract: Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. There is a wide range of techniques to describe and compare time series, but they focus on series' values. This paper concentrates on a new aspect-that of describing oscillation patterns. It presents a technique for time series similarity search, and multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. This technique is generalized to handle co-evolution, in which several phenomena vary at the same time. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of single time series, for multiple time scales, and is able to compute the similarity among sets of co-evolving series.
Subject: Time series similarity computation
Time series descriptor
Oscillation of series
Series co-evolution
Country: Holanda
Editor: Springer
Rights: fechado
Identifier DOI: 10.1007/s10707-010-0112-5
Date Issue: 2011
Appears in Collections:Unicamp - Artigos e Outros Documentos

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