Improving the Efficiency of Traditional DTW Accelerators - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Knowledge and Information Systems (KAIS) Year : 2015

Improving the Efficiency of Traditional DTW Accelerators

Romain Tavenard
Laurent Amsaleg

Abstract

Dynamic Time Warping (DTW) is the most popular approach for evaluating the similarity of time series, but its computation is costly. Therefore, simple functions lower bounding DTW distances have been designed, accelerating searches by quickly pruning sequences that could not possibly be best matches. The tighter the bounds, the more they prune and the better the performance. Designing new functions that are even tighter is di fficult because their computation is likely to become complex, canceling the benefits of their pruning. It is possible, however, to design simple functions with a higher pruning power by relaxing the no false dismissal assumption, resulting in approximate lower bound functions. This paper describes how very popular approaches accelerating DTW such as LB Keogh and LB PAA can be made more e fficient via approximations. The accuracy of approximations can be tuned, ranging from no false dismissal to potential losses when aggressively set for great response time savings. At very large scale, indexing time series is mandatory. This paper also describes how approximate lower bound functions can be used with iSAX. Furthermore, it shows that a k-means-based quantization step for iSAX gives signi ficant performance gains.
Fichier principal
Vignette du fichier
hal-00862176.pdf (542.24 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00862176 , version 1 (22-02-2017)

Identifiers

Cite

Romain Tavenard, Laurent Amsaleg. Improving the Efficiency of Traditional DTW Accelerators. Knowledge and Information Systems (KAIS), 2015, 42 (1), pp.215-243. ⟨10.1007/s10115-013-0698-7⟩. ⟨hal-00862176⟩
228 View
228 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More