Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

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

We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

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 is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

The automatic supervision of IT systems is a current challenge at Orange. Given the size and
complexity reached by its IT operations, the number of sensors needed to obtain …

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

With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …