Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …
systems continues to generate massive amounts of data. Many approaches have been …
Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
Self-supervised contrastive pre-training for time series via time-frequency consistency
Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …
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 …
Deep learning for time series anomaly detection: A survey
Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …
applications, including financial markets, economics, earth sciences, manufacturing, and …
Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding
Anomaly detection is a crucial task for monitoring various status (ie, metrics) of entities (eg,
manufacturing systems and Internet services), which are often characterized by multivariate …
manufacturing systems and Internet services), which are often characterized by multivariate …
MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection
Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …
stability of working devices (eg, water treatment system and spacecraft), whose data are …
Practical approach to asynchronous multivariate time series anomaly detection and localization
A Abdulaal, Z Liu, T Lancewicki - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Engineers at eBay utilize robust methods in monitoring IT system signals for anomalies.
However, the growing scale of signals, both in volumes and dimensions, overpowers …
However, the growing scale of signals, both in volumes and dimensions, overpowers …
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach
Y Song, R Xin, P Chen, R Zhang, J Chen… - Future Generation …, 2023 - Elsevier
Cloud computing provides scalable and elastic resources to customers as a low-cost, on-
demand utility service. Multivariate time series anomaly detection is crucial to promise the …
demand utility service. Multivariate time series anomaly detection is crucial to promise the …