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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)
- Paper ID
- 13876
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,601 | 26.26%
- DOI
-
10.14778/3705829.3705841
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Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 4,113 |
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6.4420064e-05 |
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5.7188461e-05 |
| 5,438 |
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2024 |
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| 6,330 |
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| 6,589 |
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| 8,386 |
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4.530141e-05 |
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