Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles
Summary: Unsupervised time series outlier detection via a diversity-driven convolutional ensemble of seq2seq autoencoders. Diversity-aware training preserves variety to boost accuracy; parallel training and parameter transfer speed up learning on multivariate data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. David Campos (Aalborg University)
- 2. Tung Kieu (Aalborg University)
- 3. Chenjuan Guo (Aalborg University)
- 4. Feiteng Huang (Huawei Cloud Database Innovation Lab)
- 5. Kai Zheng (University of Electronic Science and Technology of China)
- 6. Bin Yang (Aalborg University)
- 7. Christian S. Jensen (Aalborg University)
BibTeX Citation
@article{campos_vldb22,
title = {{Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles}},
author = {Campos, David and Kieu, Tung and Guo, Chenjuan and Huang, Feiteng and Zheng, Kai and Yang, Bin and Jensen, Christian S.},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {3},
pages = {611--623},
doi = {10.14778/3494124.3494142},
url = {https://doi.org/10.14778/3494124.3494142},
year = {2022}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 142 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.0002962566 |
| 2,756 | NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing | 2019 | VLDB | 8.1604054e-05 |
| 2,995 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB | 7.8750141e-05 |
| 3,135 | Real-Time Distance-Based Outlier Detection in Data Streams | 2021 | VLDB | 7.7214053e-05 |
| 3,694 | Anytime Stochastic Routing with Hybrid Learning | 2020 | VLDB | 7.1937882e-05 |
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