AutoAI-TS: AutoAI for Time Series Forecasting
Summary: Zero-config AutoAI-TS automatically trains, optimizes, and selects forecasting pipelines spanning statistical, ML, hybrid, and DL models with autonomous data prep. It uses T-Daub to rank pipelines; benchmarks show state-of-the-art performance without manual tuning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Syed Yousaf Shah
- 2. Dhaval Patel
- 3. Long Vu
- 4. Xuan-Hong Dang
- 5. Bei Chen
- 6. Peter Kirchner
- 7. Horst Samulowitz
- 8. David Wood
- 9. Gregory Bramble
- 10. Wesley M. Gifford
- 11. Giridhar Ganapavarapu
- 12. Roman Vaculin
- 13. Petros Zerfos
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,934 | SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting | 2023 | VLDB | 6.6175631e-05 |
| 6,589 | AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting | 2023 | SIGMOD | 5.001285e-05 |
| 11,059 | DARKER: Efficient Transformer with Data-driven Attention Mechanism for Time Series | 2024 | VLDB | 4.1945683e-05 |
| 11,108 | A Demonstration of TENDS: Time Series Management System based on Model Selection | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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