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 …

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 is widely derived from industrial facilities, such as power plants,
manufacturing machines, spacecraft, digital devices, and so on, and anomaly detection and …

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 …

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 …

Anomaly detection on multivariate time series (MTS) is an important research topic in data
mining, which has a wide range of applications in information technology, financial …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …

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 …

More recently, smart agriculture has received widespread attention, which is a deep
combination of modern agriculture and the Internet of Things (IoT) technology. To achieve …

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 …