Time series anomaly detection is widely used to monitor the equipment sates through the
data collected in the form of time series. At present, the deep learning method based on …

Anomaly detection is a classical but worthwhile problem, and many deep learning-based
anomaly detection algorithms have been proposed, which can usually achieve better …

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 …

Anomaly detection in time series data is a significant problem faced in many application
areas such as manufacturing, medical imaging and cyber-security. Recently, Generative …

For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to
build smart industry. Especially, explosive time-series data pose enormous challenges to the …

Time series anomaly detection is an important part of Prognostic and Health Management
(PHM), and has been widely studied and followed with interest. The data with time series …

Detecting anomalies in time series is a vital technique in a wide variety of industrial
application in which sensors monitor expensive machinery. The complexity of this task …

Multivariate time series anomaly detection has great potentials in many practical
applications such as structural health monitoring, intelligent operation and maintenance …

Recently, as real-time sensor data collection increases in various fields such as power
plants, smart factories, and health care systems, anomaly detection for multivariate time …

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 …