RITA: Group Attention is All You Need for Timeseries Analytics
Summary: RITA uses group attention to scale time-series transformers by clustering similar series into few groups and attending groupwise. Dynamic scheduler adapts group count and batch size to meet quality guarantees, delivering up to 63× speedups with accuracy. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jiaming Liang
- 2. Lei Cao
- 3. Samuel Madden
- 4. Zachary Ives
- 5. Guoliang Li
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 362 | Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases | 1995 | VLDB | 0.00025758421 |
| 494 | Milvus: A Purpose-Built Vector Data Management System | 2021 | SIGMOD | 0.00021769407 |
| 2,831 | Smile: A System to Support Machine Learning on EEG Data at Scale | 2019 | VLDB | 8.0485807e-05 |
| 4,062 | GRAIL: Efficient Time-Series Representation Learning | 2019 | VLDB | 6.4792249e-05 |
| 4,110 | RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection | 2021 | SIGMOD | 6.4358092e-05 |
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