A Novel Technique to Time Series Shapelets

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Time Series Shapelets



  1. A pool of time series, of different length

  2. They are from class A and B, labeled



       Find a series of shapelets to optimally split the set



  1. Supervised learning (explicit labeling), training / testing

  2. Decision tree (find an optimal shapelet at each node)

  3. Euclidean distance (basically distance measure only computed for subsequences with the same length)

  4. Some modification applied to brute force method to reduce complexity and storage











Brute force algorithm:







1. Early abandon (faster)

2. Admissible entropy pruning (faster)



Classification by decision tree:


Impurity measures: entropy, Gini, classification error



Comments for our problem:


  1. We don’t need to find an optimal shapelet: our reference subsequence is already quite short

  2. We can break down the long time series into small pieces, and regard it as a pool, but we do not have explicit labels, it is not a supervised learning problem.


Source: Ye, L. & Keogh, E. Data Min Knowl Disc (2011) 22: 149. https://doi.org/10.1007/s10618-010-0179-5



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