DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuoustime domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on S5 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks.
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This content will become publicly available on May 1, 2025
Enhancing Real-time Inference Performance for Time-Critical Software-Defined Vehicles
- Award ID(s):
- 2140346
- PAR ID:
- 10552176
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-0773-3
- Page Range / eLocation ID:
- 101 to 113
- Format(s):
- Medium: X
- Location:
- Dallas, TX, USA
- Sponsoring Org:
- National Science Foundation