Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
Pick and choose: a GNN-based imbalanced learning approach for fraud detection
Graph-based fraud detection approaches have escalated lots of attention recently due to the
abundant relational information of graph-structured data, which may be beneficial for the …
abundant relational information of graph-structured data, which may be beneficial for the …
TSB-UAD: an end-to-end benchmark suite for univariate time-series anomaly detection
The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
M Ma, L Han, C Zhou - Advanced Engineering Informatics, 2023 - Elsevier
In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …
be performed efficiently and accurately, it will bring great security risks to industrial systems …
SAND: streaming subsequence anomaly detection
With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …
methods need to operate over streams of values and handle drifts in data distribution …
Series2graph: Graph-based subsequence anomaly detection for time series
P Boniol, T Palpanas - arXiv preprint arXiv:2207.12208, 2022 - arxiv.org
Subsequence anomaly detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches proposed so far in the …
applications in a wide range of domains. However, the approaches proposed so far in the …
Lte4g: Long-tail experts for graph neural networks
Existing Graph Neural Networks (GNN s) usually assume a balanced situation where both
the class distribution and the node degree distribution are balanced. However, in real-world …
the class distribution and the node degree distribution are balanced. However, in real-world …
Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection
Anomaly detection in multivariate time series data is of paramount importance for ensuring
the efficient operation of large-scale systems across diverse domains. However, accurately …
the efficient operation of large-scale systems across diverse domains. However, accurately …
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …
Unsupervised and scalable subsequence anomaly detection in large data series
Subsequence anomaly (or outlier) detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches that have been …
applications in a wide range of domains. However, the approaches that have been …