Sintel: A Machine Learning Framework to Extract Insights from Signals
Summary: Sintel: an end-to-end ML framework for time-series anomaly detection, unifying analysis, comparison, and logging. Human-in-the-loop annotations refine the pipeline, and the toolkit ships open code, data, and a spacecraft anomaly use case. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,777 | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | 2024 | VLDB | 5.3308813e-05 |
| 10,569 | Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains | 2025 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,634 | Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series | 2021 | VLDB | 0.00011058945 |
| 2,825 | Smile: A System to Support Machine Learning on EEG Data at Scale | 2019 | VLDB | 8.0563426e-05 |
| 7,311 | The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development | 2020 | SIGMOD | 4.7656884e-05 |
Previous
Page 1 / 1
Next