Trainable Time Warping: Aligning TimeSeries in the ContinuousTime Domain
Abstract
DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of timeseries. Although there have been algorithms developed that are linear in the number of timeseries, they are generally quadratic in timeseries 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, highquality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of timeseries. TTW performs alignment in the continuoustime domain using a sinc convolutional kernel and a gradientbased optimization technique. We compare TTW and GTW on 85 UCR datasets in timeseries 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.
 Publication:

arXiv eprints
 Pub Date:
 March 2019
 arXiv:
 arXiv:1903.09245
 Bibcode:
 2019arXiv190309245K
 Keywords:

 Computer Science  Machine Learning;
 Computer Science  Artificial Intelligence;
 Computer Science  Computational Complexity;
 Statistics  Machine Learning
 EPrint:
 ICASSP 2019