AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification

AdaPDTW: An Efficient Abstract-Adaptive Piecewise Dynamic Time Warping for Time Series Classification

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 unif...

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Bibliographic Details
Main Authors: Qinglin Cai, Leiying Chen, Jian Shao, Ling Chen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Time series
distance metric learning
dynamic time warping
classification
Online Access:https://ieeexplore.ieee.org/document/10994439/
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https://ieeexplore.ieee.org/document/10994439/

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