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
- 11392
- Venue
- VLDB
- Year
- 2017
- Pagerank
- 7.4905759e-05
- Overall Rank
- 3,142 | 78.17%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 18 of 18 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,828 |
Cleanits: A Data Cleaning System for Industrial Time Series |
2019 |
VLDB |
6.7191138e-05 |
| 3,971 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5733348e-05 |
| 5,479 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4849266e-05 |
| 6,731 |
ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams |
2021 |
VLDB |
4.9436119e-05 |
| 6,903 |
Kamel: A Scalable BERT-based System for Trajectory Imputation |
2024 |
VLDB |
4.8878659e-05 |
| 7,221 |
Akane: Perplexity-Guided Time Series Data Cleaning |
2024 |
SIGMOD |
4.7919849e-05 |
| 8,008 |
Online Topic-Aware Entity Resolution Over Incomplete Data Streams |
2021 |
SIGMOD |
4.6037276e-05 |
| 8,096 |
Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications |
2023 |
SIGMOD |
4.583522e-05 |
| 8,154 |
Why Not Match: On Explanations of Event Pattern Queries |
2021 |
SIGMOD |
4.5708988e-05 |
| 8,161 |
TOD: GPU-accelerated Outlier Detection via Tensor Operations |
2023 |
VLDB |
4.5688249e-05 |
| 8,287 |
QARTA: An ML-based System for Accurate Map Services |
2021 |
VLDB |
4.5392079e-05 |
| 9,558 |
Clean4TSDB: A Data Cleaning Tool for Time Series Databases |
2024 |
VLDB |
4.3212967e-05 |
| 9,560 |
MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data |
2024 |
VLDB |
4.3212967e-05 |
| 9,797 |
Distance-based Outlier Query Optimization in Apache IoTDB |
2024 |
VLDB |
4.2777144e-05 |
| 10,061 |
Cleaning Time Series under Seasonal and Trend Constraints |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,682 |
Improving Time Series Data Compression in Apache IoTDB |
2025 |
VLDB |
4.1905499e-05 |
| 10,968 |
High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors |
2024 |
SIGMOD |
4.1905499e-05 |
| 11,540 |
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization |
2021 |
VLDB |
4.1905499e-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 |
| 11,889 |
Cleaning Timestamps with Temporal Constraints |
2016 |
VLDB |
4.1905499e-05 |
| 10,520 |
The Best of Both Worlds: On Repairing Timestamps and Attribute Values for Multivariate Time Series |
2025 |
SIGMOD |
4.1905499e-05 |
| 9,560 |
MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data |
2024 |
VLDB |
4.3212967e-05 |
| 6,435 |
An Experimental Evaluation of Anomaly Detection in Time Series |
2024 |
VLDB |
5.0555305e-05 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |
| 10,081 |
From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies |
2026 |
SIGMOD |
4.1905499e-05 |
| 4,991 |
Sequential Data Cleaning: A Statistical Approach |
2016 |
SIGMOD |
5.7754141e-05 |
| 9,048 |
On Repairing Timestamps for Regular Interval Time Series |
2022 |
VLDB |
4.3997447e-05 |
| 6,447 |
Multivariate Time Series Cleaning under Speed Constraints |
2024 |
SIGMOD |
5.0534784e-05 |
| 10,061 |
Cleaning Time Series under Seasonal and Trend Constraints |
2026 |
SIGMOD |
4.1905499e-05 |