Back to papers
Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing
Summary: Proposes iterative minimum repairing (IMR) for time series, repairing anomalies via temporal context and the minimum-change principle. Convergence analysis and incremental O(1) per-iteration parameter estimation reduce cost from O(n) to O(1); real-data experiments show superior repair and improved time-series classification.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 11391
- Venue
- VLDB
- Year
- 2017
- Pagerank
- 7.4978041e-05
- Overall Rank
- 3,133 | 78.21%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 18 of 18 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,825 |
Cleanits: A Data Cleaning System for Industrial Time Series |
2019 |
VLDB |
6.7255837e-05 |
| 3,967 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5796647e-05 |
| 5,468 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4902013e-05 |
| 6,727 |
ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams |
2021 |
VLDB |
4.9483604e-05 |
| 6,900 |
Kamel: A Scalable BERT-based System for Trajectory Imputation |
2024 |
VLDB |
4.8925595e-05 |
| 7,223 |
Akane: Perplexity-Guided Time Series Data Cleaning |
2024 |
SIGMOD |
4.7965857e-05 |
| 8,005 |
Online Topic-Aware Entity Resolution Over Incomplete Data Streams |
2021 |
SIGMOD |
4.6081461e-05 |
| 8,092 |
Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications |
2023 |
SIGMOD |
4.587921e-05 |
| 8,149 |
Why Not Match: On Explanations of Event Pattern Queries |
2021 |
SIGMOD |
4.5752863e-05 |
| 8,157 |
TOD: GPU-accelerated Outlier Detection via Tensor Operations |
2023 |
VLDB |
4.5730908e-05 |
| 8,294 |
QARTA: An ML-based System for Accurate Map Services |
2021 |
VLDB |
4.5435639e-05 |
| 9,558 |
Clean4TSDB: A Data Cleaning Tool for Time Series Databases |
2024 |
VLDB |
4.3254416e-05 |
| 9,560 |
MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data |
2024 |
VLDB |
4.3254416e-05 |
| 9,794 |
Distance-based Outlier Query Optimization in Apache IoTDB |
2024 |
VLDB |
4.2818172e-05 |
| 10,061 |
Cleaning Time Series under Seasonal and Trend Constraints |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,674 |
Improving Time Series Data Compression in Apache IoTDB |
2025 |
VLDB |
4.1945683e-05 |
| 10,965 |
High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors |
2024 |
SIGMOD |
4.1945683e-05 |
| 11,536 |
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization |
2021 |
VLDB |
4.1945683e-05 |
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.
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 11,881 |
Cleaning Timestamps with Temporal Constraints |
2016 |
VLDB |
4.1945683e-05 |
| 10,511 |
The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series |
2025 |
SIGMOD |
4.1945683e-05 |
| 9,560 |
MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data |
2024 |
VLDB |
4.3254416e-05 |
| 6,440 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0603878e-05 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013032074 |
| 10,081 |
From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies |
2026 |
SIGMOD |
4.1945683e-05 |
| 5,002 |
Sequential Data Cleaning: A Statistical Approach |
2016 |
SIGMOD |
5.7671075e-05 |
| 9,048 |
On Repairing Timestamps for Regular Interval Time Series |
2022 |
VLDB |
4.4039656e-05 |
| 6,451 |
Multivariate Time Series Cleaning under Speed Constraints |
2024 |
SIGMOD |
5.0583324e-05 |
| 10,061 |
Cleaning Time Series under Seasonal and Trend Constraints |
2026 |
SIGMOD |
4.1945683e-05 |