Back to papers
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
- 13568
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.3257637e-05
- Overall Rank
- 5,785 | 59.80%
- 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 |
| 159 |
LOF: Identifying Density-Based Local Outliers |
2000 |
SIGMOD |
0.00040135453 |
| 1,253 |
Anomaly Detection in Time Series: A Comprehensive Evaluation |
2022 |
VLDB |
0.00013019488 |
| 1,640 |
Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series |
2021 |
VLDB |
0.00011048873 |
| 2,032 |
SAND: Streaming Subsequence Anomaly Detection |
2021 |
VLDB |
9.7320795e-05 |
| 2,140 |
Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases |
2020 |
VLDB |
9.4565836e-05 |
| 2,289 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0922439e-05 |
| 2,381 |
TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection |
2022 |
VLDB |
8.9241557e-05 |
| 2,646 |
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series |
2020 |
VLDB |
8.3751681e-05 |
| 3,946 |
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection |
2022 |
VLDB |
6.6036232e-05 |
| 4,082 |
Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series |
2023 |
VLDB |
6.4601453e-05 |
| 5,479 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4849266e-05 |
| 6,115 |
GraphAn: Graph-based Subsequence Anomaly Detection |
2020 |
VLDB |
5.1995458e-05 |
| 6,444 |
Sintel: A Machine Learning Framework to Extract Insights from Signals |
2022 |
SIGMOD |
5.0539419e-05 |
| 7,183 |
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms |
2022 |
VLDB |
4.8026282e-05 |
| 8,085 |
A New Distributional Treatment for Time Series and An Anomaly Detection Investigation |
2022 |
VLDB |
4.5859461e-05 |
| 9,299 |
Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection |
2022 |
VLDB |
4.356626e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 5,479 |
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles |
2022 |
VLDB |
5.4849266e-05 |
| 10,745 |
TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 10,128 |
WaveStitch: Flexible and Fast Conditional Time Series Generation With Diffusion Models |
2026 |
SIGMOD |
4.1905499e-05 |
| 6,419 |
AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data |
2024 |
VLDB |
5.0621949e-05 |
| 11,097 |
Time-Series Anomaly Detection: Overview and New Trends |
2024 |
VLDB |
4.1905499e-05 |
| 2,289 |
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data |
2022 |
VLDB |
9.0922439e-05 |
| 10,880 |
MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection |
2025 |
VLDB |
4.1905499e-05 |
| 10,578 |
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains |
2025 |
VLDB |
4.1905499e-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 |