Early Random Shapelet Forest

  • Isak Karlsson
  • Panagiotis Papapetrou
  • Henrik Boström
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9956)


Early classification of time series has emerged as an increasingly important and challenging problem within signal processing, especially in domains where timely decisions are critical, such as medical diagnosis in health-care. Shapelets, i.e., discriminative sub-sequences, have been proposed for time series classification as a means to capture local and phase independent information. Recently, forests of randomized shapelet trees have been shown to produce state-of-the-art predictive performance at a low computational cost. In this work, they are extended to allow for early classification of time series. An extensive empirical investigation is presented, showing that the proposed algorithm is superior to alternative state-of-the-art approaches, in case predictive performance is considered to be more important than earliness. The algorithm allows for tuning the trade-off between accuracy and earliness, thereby supporting the generation of early classifiers that can be dynamically adapted to specific needs at low computational cost.


Time Series Predictive Performance Prediction Function Dynamic Time Warping Prediction Time 
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 work was partly supported by project High-Performance Data Mining for Drug Effect Detection at Stockholm University, funded by the Swedish Foundation for Strategic Research (IIS11-0053).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Isak Karlsson
    • 1
  • Panagiotis Papapetrou
    • 1
  • Henrik Boström
    • 1
  1. 1.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden

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