Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Scalable and Accurate Subsequence Transform

Abstract : Time series classification using phase-independent subsequences called shapelets is one of the best approaches in the state of the art. This approach is especially characterized by its interpretable property and its fast prediction time. However, given a dataset of n time series of length at most m, learning shapelets requires a computation time of O(n 2 m 4) which is too high for practical datasets. In this paper, we exploit the fact that shapelets are shared by the members of the same class to propose the SAST (Scalable and Accurate Subsequence Transform) algorithm which is interpretable, accurate and more faster than the actual state of the art shapelet algorithm. The experiments we conducted on the UCR archive datasets shown that SAST is more accurate than the state of the art Shapelet Transform algorithm on many datasets, while being significantly more scalable.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.uca.fr/hal-03087686
Contributor : Michael Mbouopda <>
Submitted on : Thursday, December 24, 2020 - 10:03:07 AM
Last modification on : Tuesday, April 27, 2021 - 5:27:08 PM

File

sast.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

  • HAL Id : hal-03087686, version 1

Collections

Citation

Michael Mbouopda •, Engelbert Mephu. Scalable and Accurate Subsequence Transform. 2021. ⟨hal-03087686⟩

Share

Metrics

Record views

68

Files downloads

13