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

As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

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

Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …

Given high-dimensional time series data (eg, sensor data), how can we detect anomalous
events, such as system faults and attacks? More challengingly, how can we do this in a way …

Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

Many real-world Internet of Things (IoT) systems, which include a variety of Internet-
connected sensory devices, produce substantial amounts of multivariate time-series data …

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly
expanding field. This growth necessitates an examination of application trends and current …

Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …