A Demonstration of TENDS: Time Series Management System based on Model Selection
Summary: TENDS is a model-selection-based time-series management system integrating imputation, prediction, anomaly detection, and visualization. Its distinctive features are adaptive selection across diverse data, 14 prediction/3 imputation methods, and an evolving expert knowledge base for online anomaly detection. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yuanyuan Yao (Zhejiang University)
- 2. Shenjia Dai (Zhejiang University)
- 3. Yilin Li (Zhejiang University)
- 4. Lu Chen (Zhejiang University)
- 5. Dimeng Li (Alibaba)
- 6. Yunjun Gao (Zhejiang University)
- 7. Tianyi Li (Aalborg University)
BibTeX Citation
@article{yao_vldb24,
title = {{A Demonstration of TENDS: Time Series Management System based on Model Selection}},
author = {Yao, Yuanyuan and Dai, Shenjia and Li, Yilin and Chen, Lu and Li, Dimeng and Gao, Yunjun and Li, Tianyi},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {4357--4360},
doi = {10.14778/3685800.3685874},
url = {https://doi.org/10.14778/3685800.3685874},
year = {2024}
}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 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,141 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD | 6.8764792e-05 |
| 5,498 | SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting | 2023 | VLDB | 6.1972571e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,356 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB |
| 2 | 11,300 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB |
| 3 | 6,141 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 4 | 9,382 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB |
| 5 | 3,987 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB |
| 6 | 7,060 | Interpretable Clustering of Multivariate Time Series with Time2Feat | 2023 | VLDB |
| 7 | 10,977 | TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB |
| 8 | 9,390 | ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data | 2024 | VLDB |
| 9 | 13,496 | Demonstration of ModelarDB: Model-Based Management of Dimensional Time Series | 2019 | SIGMOD |
| 10 | 5,986 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |