LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation

Summary: LightTS compresses large time-series ensembles into lightweight models via adaptive distillation that weights base models by strength. Yields Pareto-optimal accuracy-size tradeoffs for budgets; tested on 128 real-world datasets, it remains competitive. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6736
Venue: SIGMOD
Year: 2023
Pagerank: 5.093636e-05
Overall Rank: 11,402 | 21.78%
DOI: 10.1145/3589316

Incoming Non-self Citations Over Time

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

Authors

1. David Campos (Aalborg University)
2. Miao Zhang (Aalborg University; Harbin Engineering University)
3. Bin Yang (Aalborg University; East China Normal University)
4. Tung Kieu (Aalborg University)
5. Chenjuan Guo (Aalborg University; East China Normal University)
6. Christian S. Jensen (Aalborg University)

BibTeX Citation

@inproceedings{campos_sigmod23,
        title = {{LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation}},
        author = {Campos, David and Zhang, Miao and Yang, Bin and Kieu, Tung and Guo, Chenjuan and Jensen, Christian S.},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589316},
        url = {https://dl.acm.org/doi/10.1145/3589316},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,950	MagicScaler: Uncertainty-aware, Predictive Autoscaling	2023	VLDB	6.9980354e-05
6,078	Multiple Time Series Forecasting with Dynamic Graph Modeling	2024	VLDB	5.9850223e-05
6,443	AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting	2023	SIGMOD	5.8775356e-05
7,825	RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems	2025	VLDB	5.5367884e-05
9,382	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB	5.2755515e-05
10,808	Noise Matters: Cross Contrastive Learning for Flink Anomaly Detection	2025	VLDB	5.093636e-05
11,251	QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models	2024	VLDB	5.093636e-05
11,350	Weakly Guided Adaptation for Robust Time Series Forecasting	2024	VLDB	5.093636e-05
13,318	Fully Automated Correlated Time Series Forecasting in Minutes	2025	VLDB	-
13,320	A Memory Guided Transformer for Time Series Forecasting	2025	VLDB	-

Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2,346	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.7168932e-05
3,694	Anytime Stochastic Routing with Hybrid Learning	2020	VLDB	7.1937882e-05
4,212	Data Series Progressive Similarity Search with Probabilistic Quality Guarantees	2020	SIGMOD	6.8283344e-05
4,851	Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles	2022	VLDB	6.4803317e-05
5,043	Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information	2022	VLDB	6.3886615e-05
5,129	AutoCTS: Automated Correlated Time Series Forecasting	2022	VLDB	6.3548172e-05
6,443	AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting	2023	SIGMOD	5.8775356e-05

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