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ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Summary: ImDiffusion uses diffusion-model imputation to capture temporal and cross-variable dependencies for robust multivariate time-series anomaly detection. It leverages intermediate denoised outputs as anomaly signals, yielding SOTA accuracy/timeliness and +11.4% F1 in Microsoft production.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13567
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.3308813e-05
- Overall Rank
- 5,777 | 59.82%
- DOI
-
10.14778/3632093.3632101
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 161 |
LOF: Identifying Density-Based Local Outliers |
2000 |
SIGMOD |
0.00039846974 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013032074 |
| 1,634 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011058945 |
| 2,029 |
SAND: Streaming Subsequence Anomaly Detection |
2021 |
VLDB |
9.740868e-05 |
| 2,139 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.4640037e-05 |
| 2,290 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0934125e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9327638e-05 |
| 2,644 |
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series |
2020 |
VLDB |
8.3832357e-05 |
| 3,943 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
6.6099833e-05 |
| 4,079 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4663636e-05 |
| 5,468 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4902013e-05 |
| 6,116 |
GraphAn: Graph-based Subsequence Anomaly Detection |
2020 |
VLDB |
5.2039218e-05 |
| 6,448 |
Sintel: A Machine Learning Framework to Extract Insights from Signals |
2022 |
SIGMOD |
5.0587973e-05 |
| 7,182 |
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms |
2022 |
VLDB |
4.8072409e-05 |
| 8,083 |
A New Distributional Treatment for Time Series and An Anomaly Detection Investigation |
2022 |
VLDB |
4.5903492e-05 |
| 9,294 |
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection |
2022 |
VLDB |
4.3608061e-05 |
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6.4663636e-05 |
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| 10,569 |
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4.1945683e-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 |