Monitoring complex systems results in massive multivariate time series data, and anomaly
detection of these data is very important to maintain the normal operation of the systems …

The present-day accessibility of technology enables easy logging of both sensor values and
event logs over extended periods. In this context, detecting abnormal segments in time …

In many complex systems, devices are typically monitored and generating massive
multivariate time series. However, due to the complex patterns and little useful labeled data …

Anomaly detection, in recent years, has gained increasing attention in the research and
practice of time series processing. However, the task is particularly challenging with …

Anomaly detection is a popular task in time series analytics and researchers have, therefore,
developed a plethora of algorithms to solve it. While most algorithms focus on univariate …

The accumulation of time-series data and the absence of labels make time-series Anomaly
Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based …

Existing Multivariate time-series anomaly detection methods aim to calculate the anomaly
scores of observed sequences and learn a threshold to judge whether the input data is …

Anomaly detection on multivariate time series (MTS) is of great importance in both data
mining research and industrial applications. While a handful of anomaly detection models …

Developing new methods for detecting anomalies in time series is of great practical
significance, but progress is hindered by the difficulty of assessing the benefit of new …

Multivariate Time Series (MVTS) anomaly detection is a long-standing and challenging
research topic that has attracted tremendous research effort from both industry and …