Learning Evolvable Time-series Shapelets
Author:
Affiliation:
1. System AI Laboratory Corporate R&D Center Toshiba Corporation,Japan
2. Kyoto University,Department of Intelligence Science and Technology,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9835153/9835154/09835655.pdf?arnumber=9835655
Reference41 articles.
1. K-means++: The Advantages of Careful Seeding;arthur;SODA,2007
2. Gaussian Processes for Machine Learning
3. Rare Time Series Motif Discovery from Un-bounded Streams;begum;Proc VLDB Endow,2014
4. Towards never-ending learning from time series streams
5. Online discovery and maintenance of time series motifs
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1. TS3Net: Triple Decomposition with Spectrum Gradient for Long-Term Time Series Analysis;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13
2. A Shapelet-Based Framework for Unsupervised Multivariate Time Series Representation Learning;Proceedings of the VLDB Endowment;2023-11
3. Time-series Shapelets with Learnable Lengths;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21
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