DeepAnT: A deep learning approach for unsupervised anomaly detection in time series
Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …
periodic and seasonality related point anomalies which occur commonly in streaming data …
Clustering-based anomaly detection in multivariate time series data
Multivariate time series data come as a collection of time series describing different aspects
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
of a certain temporal phenomenon. Anomaly detection in this type of data constitutes a …
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …
industrial applications in which sensors monitor expensive machinery. The complexity of this …
Chiller fault detection and diagnosis with anomaly detective generative adversarial network
K Yan - Building and Environment, 2021 - Elsevier
Data augmentation is one of the necessary steps in the process of automated data-driven
fault detection and diagnosis (FDD) for chillers, while real-world operational training …
fault detection and diagnosis (FDD) for chillers, while real-world operational training …
Multi-step short-term power consumption forecasting with a hybrid deep learning strategy
Electric power consumption short-term forecasting for individual households is an important
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
and challenging topic in the fields of AI-enhanced energy saving, smart grid planning …
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 …
Power consumption predicting and anomaly detection based on transformer and K-means
J Zhang, H Zhang, S Ding, X Zhang - Frontiers in Energy Research, 2021 - frontiersin.org
With the advancement of technology and science, the power system is getting more
intelligent and flexible, and the way people use electric energy in their daily lives is …
intelligent and flexible, and the way people use electric energy in their daily lives is …
Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review
Performance diagnosis systems are defined as detecting abnormal performance
phenomena and play a crucial role in cloud applications. An effective performance …
phenomena and play a crucial role in cloud applications. An effective performance …
GTAD: Graph and temporal neural network for multivariate time series anomaly detection
The rapid development of smart factories, combined with the increasing complexity of
production equipment, has resulted in a large number of multivariate time series that can be …
production equipment, has resulted in a large number of multivariate time series that can be …
Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning
J He, Z Cheng, B Guo - Reliability Engineering & System Safety, 2024 - Elsevier
Anomaly detection based on telemetry data is a major issue in satellite health monitoring,
given that it can identify unusual or unexpected events to avoid serious accidents and …
given that it can identify unusual or unexpected events to avoid serious accidents and …