Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

@article{Yeh2016MatrixPI,
  title={Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets},
  author={Chin-Chia Michael Yeh and Yan Zhu and Liudmila Ulanova and Nurjahan Begum and Yifei Ding and Hoang Anh Dau and Diego Furtado Silva and Abdullah Mueen and Eamonn J. Keogh},
  journal={2016 IEEE 16th International Conference on Data Mining (ICDM)},
  year={2016},
  pages={1317-1322}
}
The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality… CONTINUE READING
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