AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification
Year: 2025
Type: article
Abstract: Dynamic Time Warping (DTW) offers precise similarity measure but suffers from high computational cost. To address this issue, we propose an abstract-adaptive PAR-DTW, which computes DTW in a low-dimensional piecewise abstract representation (PAR) space. Unlike existing methods that use globally uniform pointwise measures, our approach clusters abstracts and learns adaptive pointwise measures for each cluster pair. We formulate a joint objective function that combines global classification loss with local clustering loss, and introduce an efficient optimization method based on closed-form solutions. This enhances local adaptability while significantly reducing computational complexity. Experimental results demonstrate that our method achieves precision comparable to DTW and significantly outperforms existing PAR-DTW methods, offering up to four orders of magnitude in speedup.
Source: IEEE Access
Authors: Qinglin Cai, Leiying Chen, Jian Shao, Ling Chen
Institutions: Apple (United States), Zhejiang University
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4410226591