Fully Automated Correlated Time Series Forecasting in Minutes
Summary: Fully automated framework for correlated time-series forecasting that uses data-driven iterative pruning of human search spaces, zero-shot model search, and fast parameter adaptation to enable search and training in minutes. Matches or beats SOTA on seven benchmarks while dramatically reducing search/training time. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Xinle Wu
- 2. Xingjian Wu
- 3. Dalin Zhang
- 4. Miao Zhang
- 5. Chenjuan Guo
- 6. Bin Yang
- 7. Christian S. Jensen
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,601 | Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching | 2025 | VLDB | 4.1945683e-05 |
| 13,115 | A Memory Guided Transformer for Time Series Forecasting | 2025 | VLDB | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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