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 …
Anomaly detection in time series: a comprehensive evaluation
S Schmidl, P Wenig, T Papenbrock - Proceedings of the VLDB …, 2022 - dl.acm.org
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives
This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …
Towards a rigorous evaluation of time-series anomaly detection
In recent years, proposed studies on time-series anomaly detection (TAD) report high F1
scores on benchmark TAD datasets, giving the impression of clear improvements in TAD …
scores on benchmark TAD datasets, giving the impression of clear improvements in TAD …
DCT-GAN: dilated convolutional transformer-based GAN for time series anomaly detection
Time series anomaly detection (TSAD) is an essential problem faced in several fields, eg,
fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial …
fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial …
Robust and accurate performance anomaly detection and prediction for cloud applications: a novel ensemble learning-based framework
Effectively detecting run-time performance anomalies is crucial for clouds to identify
abnormal performance behavior and forestall future incidents. To be used for real-world …
abnormal performance behavior and forestall future incidents. To be used for real-world …
Hybrid-order representation learning for electricity theft detection
Electricity theft is the primary cause of electrical losses in power systems, which severely
harms the economic benefits of electricity providers and threatens the safety of the power …
harms the economic benefits of electricity providers and threatens the safety of the power …
Evaluating algorithms for anomaly detection in satellite telemetry data
Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its
safe operation. Although there exist various techniques for automatically detecting point …
safe operation. Although there exist various techniques for automatically detecting point …
DCFF-MTAD: a multivariate time-series anomaly detection model based on dual-channel feature fusion
Z Xu, Y Yang, X Gao, M Hu - Sensors, 2023 - mdpi.com
The detection of anomalies in multivariate time-series data is becoming increasingly
important in the automated and continuous monitoring of complex systems and devices due …
important in the automated and continuous monitoring of complex systems and devices due …
Self-supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection
Abstract Time Series Anomaly Detection (TSAD) finds widespread applications across
various domains such as financial markets, industrial production, and healthcare. Its primary …
various domains such as financial markets, industrial production, and healthcare. Its primary …