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Statistical and Numerical Algorithms for Time Series Classification

  • Roberto BaragonaEmail author
  • Salvatore Vitrano
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Cluster analysis of time series is usually performed by extracting and comparing relevant interesting features from the data. Quite a few numerical algorithms are available that search for highly separated data sets with strong internal cohesion so that some suitable objective function is minimized or maximized. Algorithms developed for classifying independent sample data are often adapted to the time series framework. On the other hand time series are dependent data and their statistical properties may serve to drive allocation of time series to groups by performing formal statistical tests. Which is the class of methods that has better chance of fulfilling its task is an open question. Some comparisons are presented concerned with the application of different algorithms to simulated time series. The data recorded for monitoring the visitors flow to archaeological areas, museums, and other sites of interest for displaying Italy’s cultural heritage are examined in some details.

Keywords

Time Series Cross Correlation Visitor Flow Autoregressive Moving Average Time Series Classification 
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.

Notes

Acknowledgements

This work was financially supported by grants from MIUR, Italy. R. Baragona gratefully acknowledges financial support from the EU Commission through MRTN-CT-2006-034270 COMISEF.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Sociology and CommunicationSapienza University of RomeRomeItaly

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