R. Agrawal, T. Imieli´nskiimieli´nski, and A. Swami, Mining association rules between sets of items in large databases, Acm sigmod record, pp.207-216, 1993.

D. J. Berndt and J. Clifford, Using dynamic time warping to find patterns in time series, KDD workshop, pp.359-370, 1994.

G. Das, K. Lin, H. Mannila, G. Renganathan, and P. Smyth, Rule discovery from time series, KDD, pp.16-22, 1998.

H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and mining of time series data, Proceedings of the VLDB Endowment, pp.1542-1552, 2008.
DOI : 10.14778/1454159.1454226

P. Esling and C. Agon, Time-series data mining, ACM Computing Surveys, vol.45, issue.1, p.12, 2012.
DOI : 10.1145/2379776.2379788
URL : https://hal.archives-ouvertes.fr/hal-01577883

S. Harms, J. Deogun, and T. Tadesse, Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences, International Symposium on Methodologies for Intelligent Systems, pp.432-441, 2002.
DOI : 10.1007/3-540-48050-1_47

M. L. Hetland and P. Saetrom, Temporal Rule Discovery using Genetic Programming and Specialized Hardware, Applications and Science in Soft Computing, pp.87-94, 2004.
DOI : 10.1007/978-3-540-45240-9_13

X. Jin, Y. Lu, and C. Shi, Distribution Discovery: Local Analysis of Temporal Rules, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.469-480, 2002.
DOI : 10.1007/3-540-47887-6_47

E. Keogh, J. Lin, and W. Truppel, Clustering of time series subsequences is meaningless: Implications for previous and future research, Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pp.115-122, 2003.

J. Lin, E. Keogh, S. Lonardi, and P. Patel, Finding motifs in time series, Proc. of the 2nd Workshop on Temporal Data Mining, pp.53-68, 2002.

M. Müller, Information retrieval for music and motion, 2007.
DOI : 10.1007/978-3-540-74048-3

J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

S. Salvador and P. Chan, Toward accurate dynamic time warping in linear time and space, Intelligent Data Analysis, vol.11, issue.5, pp.561-580, 2007.

P. S. Hyun and W. Wesley, Discovering and matching elastic rules from sequence databases, Fundamenta Informaticae, vol.47, issue.12, pp.75-90, 2001.

M. Shokoohi-yekta, Y. Chen, B. Campana, B. Hu, J. Zakaria et al., Discovery of Meaningful Rules in Time Series, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.1085-1094, 2015.
DOI : 10.1145/1007568.1007574

M. Shokoohi-yekta, B. Hu, H. Jin, J. Wang, and E. Keogh, Generalizing DTW to the multi-dimensional case requires an adaptive approach, Data Mining and Knowledge Discovery, vol.52, issue.2, pp.1-31, 2017.
DOI : 10.1109/ICDM.2010.11

D. F. Silva, G. E. Batista, and E. Keogh, On the effect of endpoints on dynamic time warping, SIGKDD Workshop on Mining and Learning from Time Series, 2016.

P. Tormene, T. Giorgino, S. Quaglini, and M. Stefanelli, Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation, Artificial Intelligence in Medicine, vol.45, issue.1, pp.11-34, 2009.
DOI : 10.1016/j.artmed.2008.11.007

H. Wu, B. Salzberg, and D. Zhang, Online event-driven subsequence matching over financial data streams, Proceedings of the 2004 ACM SIGMOD international conference on Management of data , SIGMOD '04, pp.23-34, 2004.
DOI : 10.1145/1007568.1007574