Normalization in Motif Discovery
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-25891-6_24
Reference23 articles.
1. Dau, H.A., et al.: The UCR time series archive. IEEE/CAA J. Autom. Sin. 6(6), 1293–1305 (2019). https://doi.org/10.1109/jas.2019.1911747
2. Dau, H.A., Keogh, E.: Matrix profile V: a generic technique to incorporate domain knowledge into motif discovery. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 125–134 (2017)
3. Esling, P., Agon, C.: Time-series data mining. ACM Comput. Surv. 45(1), 12 (2012)
4. Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917–963 (2019). https://doi.org/10.1007/s10618-019-00619-1
5. Gao, Y., Lin, J.: HIME: discovering variable-length motifs in large-scale time series. Knowl. Inf. Syst. 61(1), 513–542 (2018). https://doi.org/10.1007/s10115-018-1279-6
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