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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Integrating I/O Time to Virtual Time System for High Fidelity Container-based Network Emulation
- Award ID(s):
- 2113903
- PAR ID:
- 10343640
- Date Published:
- Journal Name:
- SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS),
- Page Range / eLocation ID:
- 37 to 48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3518997.3531023
