The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning time-series shapelets
Abstract
Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.
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Cites
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Ye, L., & Keogh, E. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0179-5 · Cited by 406
Cited by
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
“This observation would also be backed up by the mathematical proof provided by Cui et al. (2016), that showed how the learned filters in a CNN can be considered a generic form of shapelets extracted by the learning shapelets algorithm (Grabocka et al., 2014).”
BackgroundMethodBagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377
Multi-Scale Convolutional Neural Networks for Time Series Classification
Cui, Z., Chen, W., & Chen, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06995 · Cited by 455
Lines, J., Taylor, S., & Bagnall, A. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3182382 · Cited by 333
Transfer learning for time series classification
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8621990 · Cited by 319
Metadata
- Authors
- Grabocka, J., Schilling, N., Wistuba, M., & Schmidt-Thieme, L.
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 392-401
- OpenAlex
- W1978371851
- Cited by
- 472
- References
- 1
- FWCI
- 16.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Grabocka, J., Schilling, N., Wistuba, M., & Schmidt-Thieme, L. (2014). Learning time-series shapelets. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2623330.2623613
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
