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)
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Authors
- 1. David Campos
- 2. Miao Zhang
- 3. Bin Yang
- 4. Tung Kieu
- 5. Chenjuan Guo
- 6. Christian S. Jensen
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,388 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 8.9132902e-05 |
| 2,644 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.3832357e-05 |
| 4,536 | Data Series Progressive Similarity Search with Probabilistic Quality Guarantees | 2020 | SIGMOD | 6.104642e-05 |
| 4,947 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB | 5.8123636e-05 |
| 5,026 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 5.7528419e-05 |
| 5,468 | Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles | 2022 | VLDB | 5.4902013e-05 |
| 6,589 | AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting | 2023 | SIGMOD | 5.001285e-05 |
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