Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series
Summary: A broad, uniform benchmark re-implements and evaluates 12 time-series missing-block imputation methods across 10 diverse sensor datasets. It exposes use-case-dependent strengths and weaknesses, recommends techniques, and identifies gaps for future algorithm development. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mourad Khayati (University of Freiburg)
- 2. Alberto Lerner (University of Freiburg)
- 3. Zakhar Tymchenko (University of Freiburg)
- 4. Philippe Cudré-Mauroux (University of Freiburg)
BibTeX Citation
@article{khayati_vldb20,
title = {{Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series}},
author = {Khayati, Mourad and Lerner, Alberto and Tymchenko, Zakhar and Cudré-Mauroux, Philippe},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {5},
pages = {768--782},
doi = {10.14778/3377369.3377383},
url = {https://doi.org/10.14778/3377369.3377383},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 15 of 15 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 549 | ERACER: A Database Approach for Statistical Inference and Data Cleaning | 2010 | SIGMOD | 0.00016692839 |
| 891 | Adaptive Stream Resource Management Using Kalman Filters | 2004 | SIGMOD | 0.00013382575 |
| 1,252 | Streaming Pattern Discovery in Multiple Time-Series | 2005 | VLDB | 0.00011483752 |
| 2,208 | Query Optimization for Dynamic Imputation | 2017 | VLDB | 8.9512455e-05 |
| 2,541 | Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science | 2018 | VLDB | 8.4500033e-05 |
| 3,486 | Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach | 2018 | VLDB | 7.3696676e-05 |
| 4,684 | Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes | 2017 | VLDB | 6.5630679e-05 |
| 12,777 | Fast Algorithms for Time Series with applications to Finance, Physics, Music, Biology, and other Suspects | 2004 | SIGMOD | 5.093636e-05 |
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