The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Making Time-series Classification More Accurate Using Learned Constraints
Abstract
It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation. Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. We show the utility of our approach on datasets from diverse domains and demonstrate significant gains in accuracy and efficiency.
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Cites
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
Cited by
Three Myths about Dynamic Time Warping Data Mining
Ratanamahatana, C. A., & Keogh, E. (2005) · Proceedings of the 2005 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.50 · Cited by 393
Self-citationA 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
Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-015-4478-2 · Cited by 273
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098086 · Cited by 145
“Distance based methods include K nearest neighbor classifier [16, 26, 27, 38, 47, 49] and SVMwith local alignment kernel [32, 41, 42] by measuring the similarity between a pair of sequences.”
MethodUnivariate time series classification using information geometry
Sun, J., Yang, Y., Liu, Y., Chen, C., Rao, W., & Bai, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.05.040 · Cited by 31
Metadata
- Authors
- Ratanamahatana, C. A., & Keogh, E.
- Venue
- Proceedings of the 2004 SIAM International Conference on Data Mining
- Published
- 2004-04-22
- Pages
- 11-22
- OpenAlex
- W58346954
- Cited by
- 407
- References
- 1
- FWCI
- 14.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ratanamahatana, C. A., & Keogh, E. (2004). Making Time-series Classification More Accurate Using Learned Constraints. Proceedings of the 2004 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972740.2
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