Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of
patient-centric precision healthcare, where treatment plans are customized based on the …

Change point detection (CPD) aims to locate abrupt property changes in time series data.
Recent CPD methods demonstrated the potential of using deep learning techniques, but …

Extracting informative and meaningful temporal segments from high-dimensional wearable
sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as …

Meter error is one of the main contributing factors to unexpected fuel losses or gains in
storage tanks at service stations. Although fuel dispensers are expected to be calibrated to …

Time series data from monitoring applications reflect the physical or logical states of the
objects, which may produce time series of distinguishable characteristics in different states …

This paper presents a framework to identify and characterise anomalies in past en-route
Mode S trajectories. The technique builds upon two previous contributions introduced in …

Manufacturing process monitoring is crucial to ensure production quality. This paper
formulates the detection problem of abnormal changes in the manufacturing process as the …

A wide range of applications based on sequential data, named time series, have become
increasingly popular in recent years, mainly those based on the Internet of Things (IoT) …

The fourth industrial revolution creates ubiquitous sensor data in production plants. To
generate maximum value out of these data, reliable and precise time series-based machine …