A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Mining and Knowledge Discovery, pp.1-55, 2016.

A. Bagnall, J. Lines, W. Vickers, and E. Keogh, The uea & ucr time series classification repository

A. Bailly, S. Malinowski, R. Tavenard, L. Chapel, and T. Guyet, Dense Bag-of-Temporal-SIFT-Words for Time Series Classification, Advanced Analysis and Learning on Temporal Data, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01252726

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

J. Grabocka, N. Schilling, M. Wistuba, and L. Schmidt-thieme, Learning timeseries shapelets, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp.392-401, 2014.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

J. Lines, L. M. Davis, J. Hills, and A. Bagnall, A shapelet transform for time series classification, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp.289-297, 2012.

T. Rakthanmanon and E. Keogh, Fast shapelets: A scalable algorithm for discovering time series shapelets, pp.668-676, 2013.

X. Renard, M. Rifqi, W. Erray, and M. Detyniecki, Random-shapelet : an algorithm for fast shapelet discovery, IEEE International Conference on Data Science and Advanced Analytics, pp.1-10, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01217435

X. Renard, M. Rifqi, G. Fricout, and M. Detyniecki, EAST Representation: Fast Discriminant Temporal Patterns Discovery From Time Series. ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01350734

S. Scardapane, D. Comminiello, A. Hussain, and A. Uncini, Group sparse regularization for deep neural networks, Neurocomputing, vol.241, pp.81-89, 2017.

P. Schäfer, The BOSS is concerned with time series classification in the presence of noise, Data Mining and Knowledge Discovery, vol.29, issue.6, pp.1505-1530, 2015.

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, A sparse-group lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, pp.231-245, 2013.

R. Tavenard, tslearn: A machine learning toolkit dedicated to time-series data, 2017.

R. Tavenard, S. Malinowski, L. Chapel, A. Bailly, H. Sanchez et al., Efficient Temporal Kernels between Feature Sets for Time Series Classification, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, pp.528-543, 2017.
URL : https://hal.archives-ouvertes.fr/halshs-01561461

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

T. Tieleman and G. Hinton, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, vol.4, pp.26-31, 2012.

M. Wistuba, J. Grabocka, and L. Schmidt-thieme, Ultra-fast shapelets for time series classification, 2015.

L. Ye and E. Keogh, Time series shapelets: a new primitive for data mining, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp.947-956, 2009.