M. Fréchet, Sur quelques points du calcul fonctionnel, Thèse, Faculté des sciences de Paris, 1906.
DOI : 10.1007/BF03018603

V. M. Velichko and N. G. Zagoruyko, Automatic recognition of 200 words, International Journal of Man-Machine Studies, vol.2, issue.3, pp.223-234, 1970.
DOI : 10.1016/S0020-7373(70)80008-6

H. Sakoe and S. Chiba, A dynamic programming approach to continuous speech recognition, Proceedings of the 7th International Congress of Acoustic, pp.65-68, 1971.

R. Kaiser and W. Knight, Digital signal averaging, Journal of Magnetic Resonance (1969), vol.36, issue.2, pp.215-220, 1969.
DOI : 10.1016/0022-2364(79)90096-9

U. Hassan and M. S. Anwar, Reducing noise by repetition: introduction to signal averaging, European Journal of Physics, vol.31, issue.3, p.453, 2010.
DOI : 10.1088/0143-0807/31/3/003

F. Petitjean, A. Ketterlin, and P. Gançarski, A global averaging method for dynamic time warping, with applications to clustering, Pattern Recognition, vol.44, issue.3, pp.678-693, 2011.
DOI : 10.1016/j.patcog.2010.09.013

M. Gupta, J. Gao, C. C. Aggarwal, and J. Han, Outlier Detection for Temporal Data: A Survey, IEEE Transactions on Knowledge and Data Engineering, vol.26, issue.9, pp.2250-2267, 2014.
DOI : 10.1109/TKDE.2013.184

F. Petitjean, G. Forestier, G. Webb, A. Nicholson, Y. Chen et al., Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification, 2014 IEEE International Conference on Data Mining, pp.470-479, 2014.
DOI : 10.1109/ICDM.2014.27
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.6688

L. Chen and R. Ng, On The Marriage of Lp-norms and Edit Distance, Proceedings of the Thirtieth International Conference on Very Large Data Bases - ser. VLDB '04. VLDB Endowment, pp.792-803, 2004.
DOI : 10.1016/B978-012088469-8.50070-X

P. Marteau, Time warp edit distance with stiffness adjustment for time series matching Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.2, pp.306-318, 2009.
DOI : 10.1109/tpami.2008.76
URL : http://arxiv.org/abs/cs/0703033

L. Gupta, D. Molfese, R. Tammana, and P. Simos, Nonlinear alignment and averaging for estimating the evoked potential, IEEE Transactions on Biomedical Engineering, vol.43, issue.4, pp.348-356, 1996.
DOI : 10.1109/10.486255

K. H. Fasman and S. S. Comp, An introduction to biological sequence analysis, Methods in Mol. Biology, pp.21-42, 1998.
DOI : 10.1016/S0167-7306(08)60460-3

L. Wang and T. Jiang, On the Complexity of Multiple Sequence Alignment, Journal of Computational Biology, vol.1, issue.4, pp.337-348, 1994.
DOI : 10.1089/cmb.1994.1.337

W. Just and W. Just, Computational Complexity of Multiple Sequence Alignment with SP-Score, Journal of Computational Biology, vol.8, issue.6, pp.615-623, 1999.
DOI : 10.1089/106652701753307511

H. Carrillo and D. Lipman, The Multiple Sequence Alignment Problem in Biology, SIAM Journal on Applied Mathematics, vol.48, issue.5, pp.1073-1082, 1988.
DOI : 10.1137/0148063

V. Niennattrakul and C. Ratanamahatana, Shape averaging under Time Warping, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.626-629, 2009.
DOI : 10.1109/ECTICON.2009.5137128

W. Abdulla, D. Chow, and G. Sin, Cross-words reference template for dtw-based speech recognition systems, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, pp.1576-1579, 2003.
DOI : 10.1109/TENCON.2003.1273186

V. Hautamaki, P. Nykanen, and P. Franti, Time-series clustering by approximate prototypes, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008.
DOI : 10.1109/ICPR.2008.4761105
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.4853

]. F. Petitjean and P. Gançarski, Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment, Theoretical Computer Science, vol.414, issue.1, pp.76-91, 2012.
DOI : 10.1016/j.tcs.2011.09.029

F. Zhou and F. Torre, Canonical time warping for alignment of human behavior, Advances in Neural Information Processing Systems, pp.2286-2294, 2009.

F. Zhou and F. D. Torre, Generalized Canonical Time Warping, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.2, pp.279-294, 2016.
DOI : 10.1109/TPAMI.2015.2414429

S. Soheily-khah, A. Douzal-chouakria, and E. Gaussier, Generalized k-means-based clustering for temporal data under weighted and kernel time warp, Pattern Recognition Letters, vol.75, pp.63-69, 2016.
DOI : 10.1016/j.patrec.2016.03.007
URL : https://hal.archives-ouvertes.fr/hal-01385059

V. Niennattrakul and C. Ratanamahatana, Inaccuracies of Shape Averaging Method Using Dynamic Time Warping for Time Series Data, Lecture Notes in Computer Science, vol.4487, pp.513-520, 2007.
DOI : 10.1007/978-3-540-72584-8_68

P. Marteau and S. Gibet, On Recursive Edit Distance Kernels With Application to Time Series Classification, IEEE Transactions on Neural Networks and Learning Systems, vol.26, issue.6, pp.1-14, 2014.
DOI : 10.1109/TNNLS.2014.2333876
URL : https://hal.archives-ouvertes.fr/hal-00486916

M. Cuturi, J. Vert, O. Birkenes, and T. Matsui, A Kernel for Time Series Based on Global Alignments, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.413-416, 2007.
DOI : 10.1109/ICASSP.2007.366260
URL : http://arxiv.org/abs/cs/0610033

L. R. Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

B. Juang, On the Hidden Markov Model and Dynamic Time Warping for Speech Recognition-A Unified View, AT&T Bell Laboratories Technical Journal, vol.63, issue.7, pp.1213-1242, 1985.
DOI : 10.1002/j.1538-7305.1984.tb00034.x

S. Nakagawa and H. Nakanishi, Speaker-Independent English Consonant and Japanese Word Recognition by a Stochastic Dynamic Time Warping Method, IETE Journal of Research, vol.71, issue.1, pp.87-95, 1989.
DOI : 10.1109/ICASSP.1986.1168539

D. Chudova, S. Gaffney, and P. Smyth, Probabilistic models for joint clustering and time-warping of multidimensional curves, Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, ser. UAI'03, pp.134-141, 2003.

N. Saito, LOCAL FEATURE EXTRACTION AND ITS APPLICATIONS USING A LIBRARY OF BASES, 1994.
DOI : 10.1142/9789812813305_0005

E. J. Keogh, X. Xi, L. Wei, and C. Ratanamahatana, The UCR time series classification-clustering datasets, 2006.

M. Lichman and P. Marteau, Uci machine learning repository Available: http://archive Pulse width modulation data sets, 2007.