Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1
scores on benchmark TAD datasets, giving the impression of clear improvements in TAD …

Unmanned aerial vehicles (UAVs) have demonstrated remarkable versatility across a
spectrum of missions, and their autonomous flight and real-time control heavily rely on …

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 …

Time series data contains the amount of information to reflect the development process and
state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the …

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 …

Electricity theft is the primary cause of electrical losses in power systems, which severely
harms the economic benefits of electricity providers and threatens the safety of the power …

Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its
safe operation. Although there exist various techniques for automatically detecting point …