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arXiv · 1903.09245

Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain

Soheil Khorram·Melvin G McInnis·Emily Mower Provost
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Abstract

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 continuous-time domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on 85 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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BibTeXRIS

Soheil Khorram, Melvin G McInnis, Emily Mower Provost. 2019-03-21. Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain. https://arxiv.org/abs/1903.09245

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