Free access
Proceedings
Proceedings of the 2019 SIAM International Conference on Data Mining

Elastic bands across the path: A new framework and method to lower bound DTW

Abstract

The Nearest Neighbour algorithm coupled with the Dynamic Time Warping similarity measure (NN-DTW) is at the core of state-of-the-art classification algorithms including Ensemble of Elastic Distances and Collection of Transformation-Based Ensemble. DTW's complexity makes NN-DTW highly computationally demanding. To combat this, lower bounds to DTW are used to minimize the number of times the expensive DTW need be computed during NN-DTW search. Effective lower bounds must balance ‘time to calculate’ vs ‘tightness to DTW.‘ On the one hand, the tighter the bound the fewer the calls to the full DTW. On the other, calculating tighter bounds usually requires greater computation. Numerous lower bounds have been proposed. Different bounds provide different trade-off between computational time and tightness. In this work, we present a new class of lower bounds that are tighter than the popular Keogh lower bound, while requiring similar computation time. Our new lower bounds take advantage of the DTW boundary condition, monotonicity and continuity constraints. In contrast to most existing bounds, they remain relatively tight even for large windows. A single parameter to these new lower bounds controls the speed-tightness trade-off. We demonstrate that these new lower bounds provide an exceptional balance between computation time and tightness for the NN-DTW time series classification task, resulting in greatly improved efficiency for NN-DTW lower bound search.

Formats available

You can view the full content in the following formats:

Information & Authors

Information

Published In

cover image Proceedings
Proceedings of the 2019 SIAM International Conference on Data Mining
Pages: 522 - 530
Editors: Tanya Berger-Wolf, University of Illinois, USA and Nitesh Chawla, University of Notre Dame
ISBN (Online): 978-1-61197-567-3

History

Published online: 6 May 2019

Authors

Affiliations

Metrics & Citations

Metrics

Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited By

There are no citations for this item

View Options

View options

PDF

View PDF

Get Access

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share on social media

The SIAM Publications Library now uses SIAM Single Sign-On for individuals. If you do not have existing SIAM credentials, create your SIAM account https://my.siam.org.