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Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Petitjean, F., Forestier, G., Webb, G.I., Nicholson, A.E., Chen, Y., Keogh, E.: Dynamic time warping averaging of time series allows faster and more accurate classification. In: 2014 IEEE international conference on data mining. pp. 470–479. IEEE (2014)
2014
Earlier work this paper cites.
Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery 29
2015
Earlier work this paper cites.
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data mining and knowledge discovery 31
2017
Earlier work this paper cites.
Schäfer, P., Leser, U.: Fast and accurate time series classification with weasel. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. pp. 637–646 (2017)
2017
Earlier work this paper cites.
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline. In: 2017 International joint conference on neural networks (IJCNN). pp. 1578–1585. IEEE (2017)
2017
Earlier work this paper cites.
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE international conference on big data (Big Data). pp. 1367–1376. IEEE (2018)
2018
Earlier work this paper cites.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Data augmentation using synthetic data for time series classification with deep residual networks. In: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (2018)
2018
Earlier work this paper cites.
Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Keogh, E.: The ucr time series archive. IEEE/CAA Journal of Automatica Sinica 6
2019
Earlier work this paper cites.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data mining and knowledge discovery 33
2019
Cited alongside, same era.
Dempster, A., Petitjean, F., Webb, G.I.: Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery 34
2020
Cited alongside, same era.
Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D.F., Weber, J., Webb, G.I., Idoumghar, L., Muller, P.A., Petitjean, F.: Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery 34
2020
Cited alongside, same era.
Dempster, A., Schmidt, D.F., Webb, G.I.: Minirocket: A very fast (almost) deterministic transform for time series classification. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. pp. 248–257 (2021)
2021
Cited alongside, same era.
Dempster, A., Schmidt, D.F., Webb, G.I.: Hydra: Competing convolutional kernels for fast and accurate time series classification. Data Mining and Knowledge Discovery pp. 1–27 (2023)
2023
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2023
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Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J., Forestier, G.: Lite: Light inception with boosting techniques for time series classification. In: International Conference on Data Science and Advanced Analytics (DSAA) (2023)
2023
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2023
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Middlehurst, M., Large, J., Flynn, M., Lines, J., Bostrom, A., Bagnall, A.: Hive-cote 2.0: a new meta ensemble for time series classification. Machine Learning 110
2021
Cited alongside, same era.
Ay, E., Devanne, M., Weber, J., Forestier, G.: A study of knowledge distillation in fully convolutional network for time series classification. In: 2022 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2022)
2022
Cited alongside, same era.
Ismail-Fawaz, A., Devanne, M., Weber, J., Forestier, G.: Deep learning for time series classification using new hand-crafted convolution filters. In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022). pp. 972–981. IEEE (2022), https://doi.org/10.1109/BigData55660.2022.10020496
2022
Cited alongside, same era.
Pialla, G., Devanne, M., Weber, J., Idoumghar, L., Forestier, G.: Data augmentation for time series classification with deep learning models. In: International Workshop on Advanced Analytics and Learning on Temporal Data. pp. 117–132. Springer (2022)
2022
Cited alongside, same era.
Tan, C.W., Dempster, A., Bergmeir, C., Webb, G.I.: Multirocket: multiple pooling operators and transformations for fast and effective time series classification. Data Mining and Knowledge Discovery 36
2022
Cited alongside, same era.
Ismail-Fawaz, A., Devanne, M., Weber, J., Forestier, G.: Enhancing time series classification with self-supervised learning. In: 15th International Conference on Agents and Artificial Intelligence: ICAART 2023. pp. 1–8. INSTICC (2023), https://doi.org/10.5220/0011611300003393
2023
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Ismail-Fawaz, A., Ismail Fawaz, H., Petitjean, F., Devanne, M., Weber, J., Stefano, B., Webb, G., Forestier, G.: Shapedba: Generating effective time series prototypes using shapedtw barycenter averaging. In: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. pp. 1–8. undefined (2023), undefined
2023
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2023
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2023
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