A Bibliography of Dynamic Time Warping The papge is under construction.
This page maintains a collection of papers related to Dynamic Time Warping algorithm.
G. Al-Naymat, S. Chawla, and J. Taheri, “SparseDTW: A Novel Approach to
Speed Up Dynamic Time Warping,” in Proceedings of the Eighth
Australasian Data Mining Conference - Volume 101, 2009, pp. 117–127.
T. Bartoš and T. Skopal, “Revisiting Techniques for Lowerbounding the
Dynamic Time Warping Distance,” in Proceedings of the 5th International
Conference on Similarity Search and Applications, 2012, pp. 192–208.
D. J. Berndt and J. Clifford, “Using Dynamic Time Warping to Find
Patterns in Time Series,” in KDD Workshop, 1994, pp. 359–370.
V. Boulgouris, K. N. Plataniotis, and D. Hatzinakos, “Gait recognition
using dynamic time warping,” in IEEE 6th Workshop on Multimedia Signal
Processing, 2004., 2004, pp. 263–266.
 Y. Chen, B. Hu, E. Keogh,
and G. E. A. P. . Batista, “DTW-D: Time Series Semi-supervised Learning
from a Single Example,” in Proceedings of the 19th ACM SIGKDD
international conference on Knowledge discovery and data mining - KDD
’13, 2013, p. 383.
 S. Chu, E. Keogh, D. Hart, and M. Pazzani,
“Iterative Deepening Dynamic Time Warping for Time Series,” in
Proceedings of the 2002 SIAM International Conference on Data Mining,
2002, pp. 195–212.
 A. Corradini, “Dynamic time warping for
off-line recognition of a small gesture vocabulary,” in Proceedings
IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and
Gestures in Real-Time Systems, 2001, pp. 82–89.
 J. Gu and X.
Jin, “A Simple Approximation for Dynamic Time Warping Search in Large
Time Series Database,” in Proceedings of the 7th International
Conference on Intelligent Data Engineering and Automated Learning,
2006, pp. 841–848.
 P. Jangyodsuk, C. Conly, and V. Athitsos,
“Sign Language Recognition Using Dynamic Time Warping and Hand Shape
Distance Based on Histogram of Oriented Gradient Features,” in
Proceedings of the 7th International Conference on PErvasive
Technologies Related to Assistive Environments, 2014, pp. 50:1–50:6.
Y.-S. Jeong, M. K. Jeong, and O. A. Omitaomu, “Weighted dynamic time
warping for time series classification,” Pattern Recognit., vol. 44,
no. 9, pp. 2231–2240, Sep. 2011.
 A. Kassidas, J. F. MacGregor,
and P. A. Taylor, “Synchronization of batch trajectories using dynamic
time warping,” AIChE J., vol. 44, no. 4, pp. 864–875, 1998.
Keogh, “Exact indexing of dynamic time warping,” in Proceedings of the
28th international conference on Very Large Data Bases, 2002, pp.
 E. J. Keogh and M. J. Pazzani, “Scaling up dynamic
time warping for datamining applications,” in Proceedings of the sixth
ACM SIGKDD international conference on Knowledge discovery and data
mining - KDD ’00, 2000, pp. 285–289.
 E. J. Keogh, L. Wei, X.
Xi, S. Lee, and M. Vlachos, “LB_Keogh Supports Exact Indexing of Shapes
under Rotation Invariance with Arbitrary Representations and Distance
Measures,” in Vldb, 2006, pp. 882–893.
 S.-W. Kim, S. Park, and
W. W. Chu, “An index-based approach for similarity search supporting
time warping in large sequence databases,” in Data Engineering, 2001.
Proceedings. 17th International Conference on, 2001, pp. 607–614.
Z. M. Kovacs-Vajna, “A fingerprint verification system based on
triangular matching and dynamic time warping,” IEEE Trans. Pattern
Anal. Mach. Intell., vol. 22, no. 11, pp. 1266–1276, 2000.
Li and L. Yang, “Extensions and Relationships of Some Existing
Lower-bound Functions for Dynamic Time Warping,” J. Intell. Inf. Syst.,
vol. 43, no. 1, pp. 59–79, Aug. 2014.
 J. Lin, G. Cervone, and
P. Franzese, “Assessment of Error in Air Quality Models Using Dynamic
Time Warping,” in Proceedings of the 1st ACM SIGSPATIAL International
Workshop on Data Mining for Geoinformatics, 2010, pp. 38–44.
Myers, L. Rabiner, and A. Rosenberg, “Performance tradeoffs in dynamic
time warping algorithms for isolated word recognition,” IEEE Trans.
Acoust., vol. 28, no. 6, pp. 623–635, Dec. 1980.
 F. Petitjean,
G. Forestier, G. I. Webb, A. E. Nicholson, Y. Chen, and E. Keogh,
“Dynamic Time Warping Averaging of Time Series Allows Faster and More
Accurate Classification,” in 2014 IEEE International Conference on Data
Mining, 2014, pp. 470–479.
 L. R. Rabiner, A. E. Rosenberg, and
S. E. Levinson, “Considerations in dynamic time warping algorithms for
discrete word recognition,” J. Acoust. Soc. Am., vol. 63, no. S1, pp.
 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 Proceedings of the 18th ACM SIGKDD international
conference on Knowledge discovery and data mining - KDD ’12, 2012, p.
 T. M. Rath and R. Manmatha, “Word image matching using
dynamic time warping,” in Computer Vision and Pattern Recognition,
2003. Proceedings. 2003 IEEE Computer Society Conference on, 2003, vol.
2, pp. II–521.
 H. Sakoe and S. Chiba, “Dynamic programming
algorithm optimization for spoken word recognition,” IEEE Trans.
Acoust. Speech, Lang. Process., vol. 26, no. 1, pp. 43–50, 1978.
Y. Sakurai, C. Faloutsos, and M. Yamamuro, “Stream Monitoring under the
Time Warping Distance,” 2013 IEEE 29th Int. Conf. Data Eng., vol. 0,
pp. 1046–1055, 2007.
 S. Salvador and P. Chan, “Toward Accurate
Dynamic Time Warping in Linear Time and Space,” Intell. Data Anal.,
vol. 11, no. 5, pp. 561–580, Oct. 2007.
 D. Sart, A. Mueen, W.
Najjar, V. Niennattrakul, and E. Keogh, “Accelerating Dynamic Time
Warping Subsequnce Search with GPUs and FPGAs. ICDM 2010,” in
Proceedings - IEEE International Conference on Data Mining, ICDM, 2010,
 J. Tarango, E. Keogh, and P. Brisk, “Instruction
Set Extensions for Dynamic Time Warping,” in Proceedings of the Ninth
IEEE/ACM/IFIP International Conference on Hardware/Software Codesign
and System Synthesis, 2013, pp. 18:1–18:10.
 G. Tomasi, F. van
den Berg, and C. Andersson, “Correlation optimized warping and dynamic
time warping as preprocessing methods for chromatographic data,” J.
Chemom., vol. 18, no. 5, pp. 231–241, 2004.
 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, Jun. 2013.
N. Tselas and P. Papapetrou, “Benchmarking Dynamic Time Warping on
Nearest Neighbor Classification of Electrocardiograms,” in Proceedings
of the 7th International Conference on PErvasive Technologies Related
to Assistive Environments, 2014, pp. 4:1–4:4.
 M. Vlachos, M.
Hadjieleftheriou, D. Gunopulos, and E. Keogh, “Indexing
Multi-dimensional Time-series with Support for Multiple Distance
Measures,” in Proceedings of the Ninth ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, 2003, pp. 216–225.
 K. Wang and T. Gasser, “Alignment of curves by dynamic time warping,” Ann. Stat., vol. 25, no. 3, pp. 1251–1276, 1997.
X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E.
Keogh, “Experimental comparison of representation methods and distance
measures for time series data,” Data Min. Knowl. Discov., vol. 26, no.
2, pp. 275–309, 2013.
 Z. Wang, S. Huang, L. Wang, H. Li, Y.
Wang, and H. Yang, “Accelerating Subsequence Similarity Search Based on
Dynamic Time Warping Distance with FPGA,” in Proceedings of the
ACM/SIGDA International Symposium on Field Programmable Gate Arrays,
2013, pp. 53–62.
 B.-K. Y. B.-K. Yi, H. V. Jagadish, and C.
Faloutsos, “Efficient retrieval of similar time sequences under time
warping,” in Proceedings 14th International Conference on Data
Engineering, 1998, pp. 201–208.
 A. M. Youssef, T. K.
Abdel-Galil, E. F. El-Saadany, and M. M. A. Salama, “Disturbance
Classification Utilizing Dynamic Time Warping Classifier,” IEEE Trans.
Power Deliv., vol. 19, no. 1, pp. 272–278, Jan. 2004.
 D. Yu, X.
Yu, Q. Hu, J. Liu, and A. Wu, “Dynamic Time Warping Constraint Learning
for Large Margin Nearest Neighbor Classification,” Inf. Sci., vol. 181,
no. 13, pp. 2787–2796, Jul. 2011.
 X.-L. Zhang, Z.-G. Luo, and
M. Li, “Merge-Weighted Dynamic Time Warping for Speech Recognition,” J.
Comput. Sci. Technol., vol. 29, no. 6, pp. 1072–1082, 2014.
Zhou and M. H. Wong, “Boundary-based Lower-bound Functions for Dynamic
Time Warping and Their Indexing,” Inf. Sci., vol. 181, no. 19, pp.
4175–4196, Oct. 2011.
 Q. Zhu, G. Batista, T. Rakthanmanon, and
E. Keogh, “A Novel Approximation to Dynamic Time Warping allows Anytime
Clustering of Massive Time Series Datasets,” in Proceedings of the 2012
SIAM International Conference on Data Mining, 2012, pp. 999–1010.