Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching
Summary: TimeDC compresses large time-series datasets so models trained on condensed data achieve performance comparable to full datasets. It uses two-fold modal matching—decomposition-driven frequency matching to preserve temporal/frequency structure and curriculum training trajectory matching with an expert-buffer to cut memory/compute during condensation. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Hao Miao (Aalborg University)
- 2. Ziqiao Liu (University of Electronic Science and Technology of China)
- 3. Yan Zhao (Aalborg University)
- 4. Chenjuan Guo (East China Normal University)
- 5. Bin Yang (East China Normal University)
- 6. Kai Zheng (University of Electronic Science and Technology of China)
- 7. Christian S. Jensen (Aalborg University)
BibTeX Citation
@article{miao_vldb25,
title = {{Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching}},
author = {Miao, Hao and Liu, Ziqiao and Zhao, Yan and Guo, Chenjuan and Yang, Bin and Zheng, Kai and Jensen, Christian S.},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {2},
pages = {226--238},
doi = {10.14778/3705829.3705841},
url = {https://doi.org/10.14778/3705829.3705841},
year = {2025}
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|---|---|---|---|---|
| 10,883 | TEAM: Topological Evolution-aware Framework for Traffic Forecasting | 2025 | VLDB | 5.093636e-05 |
| 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB | - |
| 13,320 | A Memory Guided Transformer for Time Series Forecasting | 2025 | VLDB | - |
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