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 …
[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
Variational transformer-based anomaly detection approach for multivariate time series
X Wang, D Pi, X Zhang, H Liu, C Guo - Measurement, 2022 - Elsevier
Due to the strategic importance of satellites, the safety and reliability of satellites have
become more important. Sensors that monitor satellites generate lots of multivariate time …
become more important. Sensors that monitor satellites generate lots of multivariate time …
Correlation-aware spatial–temporal graph learning for multivariate time-series anomaly detection
Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …
including retail, transportation, power grid, and water treatment plants. Existing approaches …
A semi-supervised vae based active anomaly detection framework in multivariate time series for online systems
Nowadays, the large online systems are constructed on the basis of microservice
architecture. A failure in this architecture may cause a series of failures due to the fault …
architecture. A failure in this architecture may cause a series of failures due to the fault …
Navigating the metric maze: A taxonomy of evaluation metrics for anomaly detection in time series
The field of time series anomaly detection is constantly advancing, with several methods
available, making it a challenge to determine the most appropriate method for a specific …
available, making it a challenge to determine the most appropriate method for a specific …
A dynamic ensemble algorithm for anomaly detection in IoT imbalanced data streams
With the rapid development of ambient intelligence (AmI) in the Internet of Things (IoT),
many data streams are generated from sensing devices in intelligent scenarios. Due to the …
many data streams are generated from sensing devices in intelligent scenarios. Due to the …
Efficient kpi anomaly detection through transfer learning for large-scale web services
Timely anomaly detection of key performance indicators (KPIs), eg, service response time,
error rate, is of utmost importance to Web services. Over the years, many unsupervised deep …
error rate, is of utmost importance to Web services. Over the years, many unsupervised deep …
Deep probabilistic graphical modeling for robust multivariate time series anomaly detection with missing data
Multivariate time series anomaly detection with missing data is one of the most pending
issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data …
issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data …
Pregan: Preemptive migration prediction network for proactive fault-tolerant edge computing
Building a fault-tolerant edge system that can quickly react to node overloads or failures is
challenging due to the unreliability of edge devices and the strict service deadlines of …
challenging due to the unreliability of edge devices and the strict service deadlines of …