This page maintains a collection of papers related to Dynamic Time Warping algorithm.

[1] 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.

[2] 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.

[3] D. J. Berndt and J. Clifford, “Using Dynamic Time Warping to Find Patterns in Time Series,” in KDD Workshop, 1994, pp. 359–370.

[4] N. 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] E. Keogh, “Exact indexing of dynamic time warping,” in Proceedings of the 28th international conference on Very Large Data Bases, 2002, pp. 406–417.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

[17] H. 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.

[18] 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.

[19] C. 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.

[20] 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.

[21] 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. S79–S79, 1978.

[22] 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. 262.

[23] 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.

[24] 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.

[25] 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.

[26] 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.

[27] 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, pp. 1001–1006.

[28] 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.

[29] 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.

[30] 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.

[31] 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.

[32] 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.

[33] K. Wang and T. Gasser, “Alignment of curves by dynamic time warping,” Ann. Stat., vol. 25, no. 3, pp. 1251–1276, 1997.

[34] 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.

[35] 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.

[36] 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.

[37] 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.

[38] 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.

[39] 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.

[40] M. 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.

[41] 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.