P. Esling and C. Agon, Time-series data mining, ACM Comput. Surv., vol. 45, no. 1, p. 12, 2012.
C. T. Li, Y. Y. Yuan, and R. Wilson, An unsupervised conditional random fields approach for clustering gene expression time series, Bioinformatics, vol. 24, no. 21, pp. 2467-2473, 2008.
K. P. Chan and A. W. C. Fu, Efficient time series matching by wavelets, in Proc. 15th Int. Conf. Data Engineering, Sydney, Australia, 1999, pp. 126-133.
X. P. Ge and P. Smyth, Deformable Markov model templates for time-series pattern matching, in Proc. 6th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Boston, MA, USA, 2000, pp. 81-90.
K. Kalpakis, D. Gada, and V. Puttagunta, Distance measures for effective clustering of ARIMA time-series, in Proc. 2001 IEEE Int. Conf. Data Mining, San Jose, CA, USA, 2001, pp. 273-280.
J. Lin, E. Keogh, S. Lonardi, and P. Patel, Finding motifs in time series, in Proc. 2nd Workshop on Temporal Data Mining, Edmonton, Canada, 2002, pp. 53-68.
B. Chiu, E. Keogh, and S. Lonardi, Probabilistic discovery of time series motifs, in Proceedings of the 9th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Washington, DC, USA, 2003, pp. 493-498.
G. Das, K. I. Lin, H. Mannila, G. Renganathan, and P. Smyth, Rule discovery from time series, in Proc. 4th Int. Conf. Knowledge Discovery and Data Mining, Menlo Park, CA, USA, 1998, pp. 16-22.
F. Höppner, Discovery of temporal patterns, in Principles of Data Mining and Knowledge Discovery, L. De Raedt and A. Siebes, eds. Springer, 2001, pp. 192-203.
E. J. Keogh and M. J. Pazzani, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback, in Proc. 4th Int. Conf. Knowledge Discovery and Data Mining, New York, NY, USA, 1998, pp. 239-243.
M. Hegland, W. Clarke, and M. Kahn, Mining the MACHO dataset, Comput. Phys. Commun., vol. 142, nos. 1-3, pp. 22-28, 2001.
D. Dasgupta and S. Forrest, Novelty detection in time series data using ideas from immunology, in Proc. 8th Int. Conf. Intelligent Systems, Denver, CO, USA, 1999, pp. 82-87.
J. W. Han, G. Z. Dong, and Y. W. Yin, Efficient mining of partial periodic patterns in time series database, in Proc. 15th Int. Conf. Data Engineering, Sydney, Australia, 1999, pp. 106-115.
M. Müller, Information Retrieval for Music and Motion. Springer, 2007.
C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, Fast subsequence matching in time-series databases, ACM SIGMOD Rec., vol. 23, no. 2, pp. 419-429, 1994.
Y. Sakurai, C. Faloutsos, and M. Yamamuro, Stream monitoring under the time warping distance, in Proc. 23rd Int. Conf. Data Engineering, Istanbul, Turkey, 2007, pp. 1046-1055.
T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping, in Proc. 18th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 262-270.
X. J. Wei, C. T. Li, Z. Lei, D. Yi, and S. Z. Li, Dynamic image-to-class warping for occluded face recognition, IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2035-2050, 2014.
M. Toyoda and Y. Sakurai, Discovery of cross-similarity in data streams, in Proc. 26th Int. Conf. Data Engineering, Long Beach, CA, USA, 2010, pp. 101-104.
T. Nishii, T. Hiroyasu, M. Yoshimi, M. Miki, and H. Yokouchi, Similar subsequence retrieval from two time series data using homology search, in Proc. 2010 IEEE Int. Conf. Systems Man and Cybernetics, Istanbul, Turkey, 2010, pp. 1062-1067.
L. M. Xiao, Y. Zheng, W. Q. Tang, G. C. Yao, and L. Ruan, Parallelizing dynamic time warping algorithm using prefix computations on GPU, in Proc. 10th Int. Conf. High Performance Computing and Communications & 2013 IEEE Int. Conf. Embedded and Ubiquitous Computing, Zhangjiajie, China, 2013, pp. 294-299.
D. Sart, A. Mueen, W. Najjar, E. Keogh, and V. Niennattrakul, Accelerating dynamic time warping subsequence search with GPUs and FPGAs, in Proc. 10th Int. Conf. Data Mining, Sydney, Australia, 2010, pp. 1001-1006.
Y. D. Zhang, K. Adl, and J. Glass, Fast spoken query detection using lower-bound dynamic time warping on graphical processing units, in Proc. 2012 IEEE Int. Conf. Acoustics, Speech and Signal Processing, Kyoto, Japan, 2012, pp. 5173-5176.
A. Vahdatpour, N. Amini, and M. Sarrafzadeh, Toward unsupervised activity discovery using multi-dimensional motif detection in time series, in Proc. 21st Int. Joint Conf. Artificial Intelligence, Pasadena, CA, USA, 2009, pp. 1261-1266.
A. Mueen, E. J. Keogh, Q. Zhu, S. Cash, and M. B. Westover, Exact discovery of time series motifs, in 2009 SIAM Int. Conf. Data Mining, Sparks, NV, USA, 2009, pp. 473-484.
M. Toyoda, Y. Sakurai, and Y. Ishikawa, Pattern discovery in data streams under the time warping distance, VLDB J., vol. 22, no. 3, pp. 295-318, 2013.
S. Srikanthan, A. Kumar, and R. Gupta, Implementing the dynamic time warping algorithm in multithreaded environments for real time and unsupervised pattern discovery, in Proc. 2nd Int. Conf. Computer and Communication Technology, Allahabad, India, 2011, pp. 394-398.
N. Takahashi, T. Yoshihisa, Y. Sakurai, and M. Kanazawa, A parallelized data stream processing system using dynamic time warping distance, in Proc. 2009 Int. Conf. Complex, Intelligent and Software Intensive Systems, Fukuoka, Japan, 2009, pp. 1100-1105.
NVIDIA Corporation, NVIDIA CUDA C Programming Guide, 2011.
C. Reiss, J. Wilkes, and J. L. Hellerstein, Google cluster-usage traces: Format + Schema, Technical report, Google Inc., Mountain View, CA, USA, November 2011, revised 2014-11-17 for version 2.1, https://github.com/google/cluster-data.
R. S. Stankovic and B. J. Falkowski, The Haar wavelet transform: Its status and achievements, Comput. Electr. Eng., vol. 29, no. 1, pp. 25-44, 2003.