, Volume 15, Issue 1, pp 75–109 | Cite as

TIDES—a new descriptor for time series oscillation behavior

  • Leonardo E. Mariote
  • Claudia Bauzer MedeirosEmail author
  • Ricardo da Silva Torres
  • Lucas M. Bueno


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.


Time series similarity computation Time series descriptor Oscillation of series Series co-evolution 



This work was partially funded by CPqD Foundation, CAPES, FAPESP, CNPq grants and CNPq projects WebMAPS and RPG. It is also being partially funded by the Microsoft Research-FAPESP Virtual institute, under the eFarms project. We thank Jeferson Lobato Fernandes for providing us with experimental data.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Leonardo E. Mariote
    • 1
  • Claudia Bauzer Medeiros
    • 1
    Email author
  • Ricardo da Silva Torres
    • 1
  • Lucas M. Bueno
    • 1
  1. 1.Institute of ComputingUniversity of Campinas—CP6176CampinasBrazil

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