A Novel Technique to Time Series Shapelets

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Time Series Shapelets

 

Setting:

  1. A pool of time series, of different length

  2. They are from class A and B, labeled

 

Goal:

       Find a series of shapelets to optimally split the set

 

Idea:

  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

 

 

Definition:

 

 

 

 

 

 

 

Brute force algorithm:

 

 

 

 

Modification:

 

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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