Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Time series anomaly detection is challenging due to the complexity and variety of patterns
that can occur. One major difficulty arises from modeling time-dependent relationships to …

Effective anomaly detection in multivariate time series data is critical to ensuring the security
of Internet of Things (IoT) devices and systems. However, building a high precision and low …

Graph neural networks (GNNs) haven proven to be an indispensable approach in modeling
complex data, in particular spatial temporal data, eg, relating to sensor data given as time …

In an increasingly technology-driven world, the security of Internet-of-Things systems has
become a top priority. This article presents a study on the implementation of security …

A critical issue in intelligent building control is detecting energy consumption anomalies
based on intelligent device status data. The building field is plagued by energy consumption …

Forecasting incoming water demand is a critical step in efficient reservoir management and
revenue optimization in large-scale cascade hydropower stations. It depends on multiple …

Anomaly detection on multivariate key performance indicators (KPIs) is a key procedure for
the quality and reliability of large-scale cyber-physical systems (CPSs). Although extensive …

This article aims to present the real-world implementation of an anomaly detection system of
a hydraulic power unit. Implementation involved the Internet of Things approach. A detailed …

In actual scenarios, industrial and cloud computing platforms usually need to monitor
equipment and traffic anomalies through multivariable time series data. However, the …