The rise of time series data availability has demanded new techniques for its automated
analysis regarding several tasks, including anomaly detection. However, even though the …

In IT monitoring systems, anomaly detection plays a vital role in detecting and alerting
unexpected behaviors timely to system operators. With the growth of signal data in both …

With the proliferation of the Internet of Things, a large amount of multivariate time series
(MTS) data is being produced daily by industrial systems, corresponding in many cases to …

Accurate detection of anomalies in multivariate time series data has attracted much attention
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …

Detecting anomalies accurately in time series data has been receiving considerable
attention due to its enormous potential for a wide array of applications. Numerous …

Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …

Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …

Unsupervised anomaly detection (UAD) of multivariate time series (MTS) aims to learn
robust representations of normal multivariate temporal patterns. Existing UAD methods try to …

Unsupervised anomaly detection on multidimensional time series data is a very important
problem due to its wide applications in many systems such as cyber-physical systems, the …

Learning temporal patterns in time series remains a challenging task up until today.
Particularly for anomaly detection in time series, it is essential to learn the underlying …