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...
Saved in:
| Main Authors: | Qinglin Cai, Leiying Chen, Jian Shao, Ling Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10994439/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Data-Adaptive Dynamic Time Warping-Based Multivariate Time Series Fuzzy Clustering
by: Qinglin Cai, et al.
Published: (2025-01-01) -
Piecewise Dynamic Time Warping-Based Subsequence Matching in Data Stream
by: Qinglin Cai, et al.
Published: (2025-01-01) -
Similarity join over multiple time series under Dynamic Time Warping
by: Bui Cong Giao
Published: (2023-10-01) -
Similarity join over multiple time series under Dynamic Time Warping
by: Bui Cong Giao
Published: (2023-10-01) -
A Novel Time-Frame Regional Collision Risk Model Based on Dynamic Time Warping
by: Zihao Liu, et al.
Published: (2025-01-01)