A Hybrid Approach to Time Series Classification with Shapelets

  • David Guijo-RubioEmail author
  • Pedro A. Gutiérrez
  • Romain Tavenard
  • Anthony Bagnall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)


Shapelets are phase independent subseries that can be used to discriminate between time series. Shapelets have proved to be very effective primitives for time series classification. The two most prominent shapelet based classification algorithms are the shapelet transform (ST) and learned shapelets (LS). One significant difference between these approaches is that ST is data driven, whereas LS searches the entire shapelet space through stochastic gradient descent. The weakness of the former is that full enumeration of possible shapelets is very time consuming. The problem with the latter is that it is very dependent on the initialisation of the shapelets. We propose hybridising the two approaches through a pipeline that includes a time constrained data driven shapelet search which is then passed to a neural network architecture of learned shapelets for tuning. The tuned shapelets are extracted and formed into a transform, which is then classified with a rotation forest. We show that this hybrid approach is significantly better than either approach in isolation, and that the resulting classifier is not significantly worse than a full shapelet search.


Time series classification Shapelets Convolutional neural networks 



This research has been partially supported by the Ministerio de Economía, Industria y Competitividad of Spain (Grant Refs. TIN2017-85887-C2-1-P and TIN2017-90567-REDT) as well as Agence Nationale de la Recherche through MATS project (ANR-18-CE23-0006). D. Guijo-Rubio’s research has been supported by the FPU Predoctoral and Short Placements Programs from Ministerio de Educación y Ciencia of Spain (Grants Ref. FPU16/02128 and EST18/00280, respectively). Some experiments used a Titan X Pascal donated by the NVIDIA Corporation.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David Guijo-Rubio
    • 1
    • 2
    Email author
  • Pedro A. Gutiérrez
    • 1
  • Romain Tavenard
    • 3
  • Anthony Bagnall
    • 2
  1. 1.Department of Computer SciencesUniversidad de CórdobaCórdobaSpain
  2. 2.University of East AngliaNorwichUK
  3. 3.Univ. Rennes, CNRS, LETG/IRISARennesFrance

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