Akane: Perplexity-Guided Time Series Data Cleaning
Summary: Akane reframes time-series cleaning as perplexity minimization: exploit recurrent patterns like token n-grams, then pick edits under a cleaning budget to lower sequence perplexity. Key novelty is perplexity-guided dirty-point detection/repair with a 4-phase framework plus budget selection and pattern-aggregation heuristics. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoyu Han
- 2. Haoran Xiong
- 3. Zhenying He
- 4. Peng Wang
- 5. Chen Wang
- 6. X. Sean Wang
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,081 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD | 4.1945683e-05 |
| 10,211 | SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning | 2026 | SIGMOD | 4.1945683e-05 |
| 10,511 | The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series | 2025 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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