The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Three Myths about Dynamic Time Warping Data Mining
Abstract
The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.
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Citations
Cites
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137
Exact indexing of dynamic time warping
Keogh, E., & Ratanamahatana, C. A. (2005) · Knowledge and Information Systems · doi:10.1007/s10115-004-0154-9 · Cited by 1,962
Self-citationLocally adaptive dimensionality reduction for indexing large time series databases
Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001) · Proceedings of the 2001 ACM SIGMOD international conference on Management of data · doi:10.1145/375663.375680 · Cited by 866
Self-citationTomasi, G., van den Berg, F., & Andersson, C. (2004) · Journal of Chemometrics · doi:10.1002/cem.859 · Cited by 709
Exact indexing of dynamic time warping
Keogh, E. (2002) · Cited by 594
Self-citationWord image matching using dynamic time warping
Rath, T. M., & Manmatha, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211511 · Cited by 574
Aligning gene expression time series with time warping algorithms
Aach, J., & Church, G. M. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.495 · Cited by 496
Making Time-series Classification More Accurate Using Learned Constraints
Ratanamahatana, C. A., & Keogh, E. (2004) · Proceedings of the 2004 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972740.2 · Cited by 407
Self-citationConsiderations in dynamic time warping algorithms for discrete word recognition
Rabiner, L., Rosenberg, A., & Levinson, S. (1978) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1978.1163164 · Cited by 369
Everything you know about Dynamic Time Warping is Wrong
Ratanamahatana, C. A., & Keogh, E. (2004) · Cited by 300
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Cited by
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342
“This form of pre-processing has also been used by Ratanamahatana and Keogh (2005) to show that the length of a time series is not an issue for TSC problems.”
MethodTime-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377
Self-citationTime series classification with ensembles of elastic distance measures
Lines, J., & Bagnall, A. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0361-2 · Cited by 522
A Bag-of-Features Framework to Classify Time Series
Baydogan, M. G., Runger, G., & Tuv, E. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.72 · Cited by 361
Metadata
- Authors
- Ratanamahatana, C. A., & Keogh, E.
- Venue
- Proceedings of the 2005 SIAM International Conference on Data Mining
- Published
- 2005-01-09
- Pages
- 506-510
- OpenAlex
- W67158865
- Cited by
- 393
- References
- 11
- FWCI
- 15.97
- Type
- conference paper
- Language
- en
- Open access
- closed
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