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) …

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 …

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

Predictive maintenance (PdM) aims the reduction of costs to increase the competitive
strength of the enterprises. It uses sensor data together with analytics techniques to optimize …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

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 …

Learning temporal patterns in time series remains a challenging task up until today.
Particularly for anomaly detection in time series, it is essential to learn the underlying …

We are seeing an enormous increase in the availability of streaming, time-series data.
Largely driven by the rise of connected real-time data sources, this data presents technical …

In recent years, the rapid development and wide application of advanced technologies have
profoundly impacted industrial manufacturing, leading to smart manufacturing (SM) …

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with
numerous sensors to capture the behavior and health of the machine. However, there are …