ImputePilot: A Graphical Model Selection Toolkit for Time Series Imputation
Summary: ImputePilot uses an AutoML graphical model trained on diverse real-world series to recommend imputation algorithms for sensor time series with contiguous gaps. Its interactive interface simulates failures and enables immediate visual comparison without manual tuning. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Yuanyuan Yao (National University of Singapore)
- 2. Zhexin Jin (Zhejiang University)
- 3. Lu Chen (Zhejiang University)
- 4. Anthony K. H. Tung (National University of Singapore)
- 5. Mourad Khayati (University of Freiburg)
BibTeX Citation
@article{yao_vldb26,
title = {{ImputePilot: A Graphical Model Selection Toolkit for Time Series Imputation}},
author = {Yao, Yuanyuan and Jin, Zhexin and Chen, Lu and Tung, Anthony K. H. and Khayati, Mourad},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4742--4745},
doi = {10.14778/3827998.3828111},
url = {https://doi.org/10.14778/3827998.3828111},
year = {2026}
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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 |
|---|---|---|---|---|
| 1,805 | Raha: A Configuration-Free Error Detection System | 2019 | SIGMOD | 9.59842e-05 |
| 2,589 | Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series | 2020 | VLDB | 8.2507594e-05 |
| 9,573 | ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data | 2024 | VLDB | 5.1571823e-05 |
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