Projects per year
Abstract
Time series classification maps time series to labels. The nearest neighbor algorithm (NN) using the Dynamic Time Warping (DTW) similarity measure is a leading algorithm for this task and a component of the current best ensemble classifiers for time series. However, NN-DTW is only a winning combination when its meta-parameter -its warping window -is learned from the training data. The warping window (WW) intuitively controls the amount of distortion allowed when comparing a pair of time series. With a training database of N time series of lengths L, a naive approach to learning theWWrequires Θ(N2 · L3) operations. This often results in NN-DTW requiring days for training on datasets containing a few thousand time series only. In this paper, we introduce FastWWSearch: an efficient and exact method to learn WW. We show on 86 datasets that our method is always faster than the state of the art, with at least one order of magnitude and up to 1000x speed-up.
Original language | English |
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Title of host publication | 2018 SIAM International Conference on Data Mining, SDM 2018 |
Subtitle of host publication | San Diego Marriott Mission Valley San Diego, California USA May 3-5, 2018 |
Editors | Martin Ester, Dino Pedreschi |
Place of Publication | Philadelphia PA USA |
Publisher | Society for Industrial & Applied Mathematics (SIAM) |
Pages | 225-233 |
Number of pages | 9 |
ISBN (Electronic) | 9781611975321 |
Publication status | Published - 2018 |
Event | SIAM International Conference on Data Mining 2018 - San Diego Marriott Mission Valley, San Diego, United States of America Duration: 3 May 2018 → 5 May 2018 https://epubs.siam.org/doi/10.1137/1.9781611975321.fm |
Conference
Conference | SIAM International Conference on Data Mining 2018 |
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Abbreviated title | SDM 18 |
Country/Territory | United States of America |
City | San Diego |
Period | 3/05/18 → 5/05/18 |
Internet address |
Projects
- 1 Finished
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Time series classification for new-generation Earth observation satellites
Petitjean, F.
1/06/17 → 31/12/20
Project: Research