The rapid spread of the Internet of Things (IoT) devices has prompted many people and
companies to adopt the IoT paradigm, as this paradigm allows the automation of several …

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use
of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The …

Time series outlier detection has been extensively studied with many advanced algorithms
proposed in the past decade. Despite these efforts, very few studies have investigated how …

Several techniques for multivariate time series anomaly detection have been proposed
recently, but a systematic comparison on a common set of datasets and metrics is lacking …

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

COVID-19 is a newly identified disease, which is very contagious and has been rapidly
spreading across different countries around the world, calling for rapid and accurate …

Due to the rapidly increasing number of Internet-connected objects, a huge amount of data
is created, stored, and shared. Depending on the use case, this data is visualized, cleaned …

Many research areas depend on group anomaly detection. The use of group anomaly
detection can maintain and provide security and privacy to the data involved. This research …

Due to the exponential growth of the Internet of Things networks and the massive amount of
time series data collected from these networks, it is essential to apply efficient methods for …

Anomaly detection aims at finding observations in a dataset that do not conform to expected
behavior. Researchers have proposed a large variety of anomaly detection algorithms and …