KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
Summary: KDSelector: a knowledge-infused, data-efficient NN-based TSAD model selector; tackles heterogeneity by avoiding a single winner. It uses history-derived knowledge and selective pruning to speed training and boost selector accuracy as a plug-in module. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Zhiyu Liang
- 2. Dongrui Cai
- 3. Chenyuan Zhang
- 4. Zheng Liang
- 5. Chen Liang
- 6. Bo Zheng
- 7. Shi Qiu
- 8. Jin Wang
- 9. Hongzhi Wang
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,253 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00013032074 |
| 4,079 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB | 6.4663636e-05 |
| 13,156 | A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning | 2024 | VLDB | - |
Previous
Page 1 / 1
Next