KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series

Summary: KDSelector enhances neural TSAD model selectors by incorporating auxiliary historical knowledge through architecture-agnostic modules. Its TSAD-specific, theoretically grounded pruning accelerates training with nearly lossless selection quality, outperforming generic data-pruning approaches. (summarized by gpt-5.6-luna on Aug 17 2026)

Paper ID: hee34854bcc2b7d6f
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,760 | 27.66%
DOI: 10.14778/3819518.3819525

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Zhiyu Liang (Harbin Engineering University)
2. Dongrui Cai (Harbin Engineering University)
3. Chenyuan Zhang (Harbin Engineering University)
4. Zheng Liang (Harbin Engineering University)
5. Chen Liang (Harbin Engineering University)
6. Shi Qiu (Central South University)
7. Jin Wang (Central South University)
8. Hongzhi Wang (Harbin Engineering University)

BibTeX Citation

@article{liang_vldb26,
        title = {{KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series}},
        author = {Liang, Zhiyu and Cai, Dongrui and Zhang, Chenyuan and Liang, Zheng and Liang, Chen and Qiu, Shi and Wang, Jin and Wang, Hongzhi},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {9},
        pages = {1935--1948},
        doi = {10.14778/3819518.3819525},
        url = {https://doi.org/10.14778/3819518.3819525},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,005	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00012590067
1,937	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.3387043e-05
3,250	How Large Language Models Will Disrupt Data Management	2023	VLDB	7.4938546e-05
3,298	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	7.4447462e-05
3,344	LittleTable: A Time-Series Database and Its Uses	2017	SIGMOD	7.4000362e-05
3,622	Apache IoTDB: A Time Series Database for IoT Applications	2023	SIGMOD	7.1536644e-05
3,726	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0701368e-05
6,340	FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification	2022	VLDB	5.8092399e-05
7,531	Time Series Representation for Visualization in Apache IoTDB	2024	SIGMOD	5.5008425e-05
7,640	KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection	2025	SIGMOD	5.4772833e-05
8,277	Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale	2022	VLDB	5.3639084e-05
9,482	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.1708619e-05
9,488	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.1708619e-05
9,873	TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis	2024	VLDB	5.1176637e-05
13,677	A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	2024	VLDB	-

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