Representative Time Series Discovery for Data Exploration
Summary: Defines similarity-bounded representative time series and the min-cardinality cover for a user-specified proportion; proves NP-hard and provides approximation algorithms. Presents a learning-based method that matches effectiveness while achieving up to 21x speedups and 101x memory reduction. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ge Lee (RMIT University)
- 2. Shixun Huang (University of Wollongong)
- 3. Zhifeng Bao (RMIT University)
- 4. Yanchang Zhao (Commonwealth Scientific and Industrial Research Organisation)
BibTeX Citation
@article{lee_vldb25,
title = {{Representative Time Series Discovery for Data Exploration}},
author = {Lee, Ge and Huang, Shixun and Bao, Zhifeng and Zhao, Yanchang},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {915--928},
doi = {10.14778/3712221.3712252},
url = {https://doi.org/10.14778/3712221.3712252},
year = {2025}
}
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