Our code is embedded within the Weka framework and the zip file contains all the necessary source code. It is also included in a Netbeans project, so you should be able to load this in directly. There is a helper class called ShapeletExamples (in the package examples) to illustrate the usage. The code includes classes for:

  • A shapelet tree algorithm based on that proposed in [1].
  • A shapelet transform algorithm as described in [4] that can use any one of four shapelet similarity measures.
  • A class to reproduce all the experiments described in [4]
  • Extended shapelet transforms that take advantage of the distance speed ups proposed in [1,2]

The latest version of the shapelet transform is available in Java from here:


And in python, you can find shapelets here:



[1] Ye, L and Keogh, E.,  Time series shapelets: a new primitive for data mining Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.

[2]Mueen, A, Keogh, E and Young, N, Logical-shapelets: An expressive primitive for time series classification Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011.

[3] Rakthanmanon, T ,  Campana, b, Mueen, a, Batista, g, Westover, m, Zhu, Q , Zakaria, Keogh, E. Searching and mining trillions of time series subsequences under dynamic time warping. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

[4] Hills, J, Lines, J, Baranauskas, E, Mapp, J, and Bagnall, A.,  Time Series Classification with Shapelets,  Journal of Data Mining and Knowledge Discovery. online first preprint.pdf, 2013.