Trading Off Accuracy for Efficiency by Randomized Greedy Warping. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 883-890, ACM, New York, NY, USA, 2016, ISBN: 978-1-4503-3739-7.
Abstract
Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for f i nding similarity between time series instances.We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification.
Links
- https://wicker.nz/nwp-acm/authorize.php?id=N10030
- http://doi.acm.org/10.1145/2851613.2851651
- doi:10.1145/2851613.2851651
BibTeX (Download)
@inproceedings{raza2016trading, title = {Trading Off Accuracy for Efficiency by Randomized Greedy Warping}, author = {Atif Raza and J\"{o}rg Wicker and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10030 http://doi.acm.org/10.1145/2851613.2851651}, doi = {10.1145/2851613.2851651}, isbn = {978-1-4503-3739-7}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing}, pages = {883-890}, publisher = {ACM}, address = {New York, NY, USA}, series = {SAC '16}, abstract = {Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for f i nding similarity between time series instances.We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification.}, keywords = {data mining, dynamic time warping, time series}, pubstate = {published}, tppubtype = {inproceedings} }