Abstract: DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows ...
TTW performs alignment in the continuous- time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on ...
Trainable time warping (TTW) [8] models the warpings using discrete sine transform (DST), i.e., sine functions as the basis functions. TTW controls the non- ...
This work introduces trainable time warping (TTW), whose complexity is linear in both the number and the length of time- series, and compares TTW and GTW on ...
Generalized time warping (GTW) is a DTW averaging algorithm that can align multiple time-series with linear complexity in the length of time-series [1]. GTW ...
Mar 21, 2019 · TTW performs alignment in the continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare ...
Trainable Time Warping: Aligning Time-series in the Continuous-time Domain. h-index: Publications. Citations. 9. By rating. CO-AUTHORS. MOST CITED AUTHORS.
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Apr 1, 2019 · Bibliographic details on Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain.
Jun 29, 2020 · In this paper, we propose a new algorithm, neural time warping (NTW) to solve MSA, which can even be utilized for one hundred time-series data.
We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. 1.