Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
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Abstract (arXiv)
Approximate variational inference has shown to be a powerful tool for modeling unknown complex probability distributions. Recent advances in the field allow us to learn probabilistic models of sequences that actively exploit spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN) to learn robot time series data. Our evaluation demonstrates that we can robustly detect anomalies both off- and on-line.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2016.
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Citing papers per year: 2021: 3; 2022: 5; 2023: 3; 2024: 0; 2025: 1.
12 papers.
- 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
- 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
- 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
- 2021Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Modelscited by 14
- 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
- 2023The voraus-AD Dataset for Anomaly Detection in Robot Applicationscited by 7
- 2023Deep Anomaly Detection on Tennessee Eastman Process Datacited by 5
- 2022Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attentioncited by 1
- 2023Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detectioncited by 1
- 2025Batch Distillation Data for Developing Machine Learning Anomaly Detection Methodscited by 1
- 2022Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densitiescited by 0
- 2022Lossy Compression for Robust Unsupervised Time-Series Anomaly Detectioncited by 0
- 2025Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods20 Oct 2025
- 2023The voraus-AD Dataset for Anomaly Detection in Robot Applications8 Nov 2023
- 2023Deep Anomaly Detection on Tennessee Eastman Process Data10 Mar 2023
- 2023Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection2 Feb 2023
- 2022Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection5 Dec 2022
- 2022Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities16 Nov 2022
- 2022Deep Learning for Time Series Anomaly Detection: A Survey9 Nov 2022
- 2022Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention8 Jun 2022
- 2022Detecting Anomalies within Time Series using Local Neural Transformations8 Feb 2022
- 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version22 Nov 2021
- 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder2 Feb 2021
- 2021Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models18 Jan 2021