Mining Time Series Data: A Selective Survey

  • Marcella CorduasEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Time series prediction and control may involve the study of massive data archive and require some kind of data mining techniques. In order to make the comparison of time series meaningful, one important question is to decide what similarity means and what features have to be extracted from a time series. This question leads to the fundamental dichotomy: (a) similarity can be based solely on time series shape; (b) similarity can be measured by looking at time series structure. This article discusses the main dissimilarity indices proposed in literature for time series data mining.


Dynamic Time Warping Compare Time Series Time Series Cluster Time Series Feature ARIMA Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by Dipartimento di Scienze Statistiche, University of Naples Federico II, and CFEPSR (Portici).


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Scienze StatisticheUniversità di Napoli Federico IINapoli(I)Italy

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