Bag of recurrence patterns representation for time-series classification

  • Nima Hatami
  • Yann Gavet
  • Johan Debayle
Theoretical Advances


Time-series classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag-of-features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of “feature words” of a data-learned dictionary. This paper proposes embedding the recurrence plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treat TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed bag of recurrence patterns, compared not only to the existing BoF models, but also to the state-of-the art algorithms.


Time-series classification (TSC) Bag of features (BoF) representation Visual words Dictionary Recurrence plots (RP) 



This research is partially supported by the French national research agency (ANR) under the PANDORE Grant with reference number ANR-14-CE28-0027.

N. Hatami also acknowledges support of the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the ANR.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.The SPIN center, École Nationale Supérieure des Mines de Saint-ÉtienneSaint-ÉtienneFrance
  2. 2.CREATIS centerINSA-Lyon and University of LyonLyonFrance

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