Binary shapelet transform for multiclass time series classification (extended version)

Research output: Chapter in Book/Report/Conference proceedingChapter


Shapelets have recently been proposed as a new primitive for time series classification. Shapelets are subseries of series that best split the data into its classes. In the original research, shapelets were found recursively within a decision tree through enumeration of the search space. Subsequent research indicated that using shapelets as the basis for transforming datasets leads to more accurate classifiers.

Both these approaches evaluate how well a shapelet splits all the classes. However, often a shapelet is most useful in distinguishing between members of the class of the series it was drawn from against all others. To assess this conjecture, we evaluate a one vs all encoding scheme. This technique simplifies the quality assessment calculations, speeds up the execution through facilitating more frequent early abandon and increases accuracy for multi-class problems. We also propose an alternative shapelet evaluation scheme which we demonstrate significantly speeds up the full search.
Original languageEnglish
Title of host publicationTransactions on Large-Scale Data- and Knowledge-Centered Systems XXXII
Subtitle of host publicationSpecial Issue on Big Data Analytics and Knowledge Discovery
EditorsAbdelkader Hameurlain, Josef Küng, Roland Wagner, Sanjay Madria, Takahiro Hara
Number of pages23
ISBN (Electronic)978-3-662-55608-5
ISBN (Print)978-3-662-55607-8
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)1869-1994
ISSN (Electronic)2510-4942


  • shapelets
  • time series classification

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