There is a need for solutions which assist users to understand long time-series data by
observing its changes over time, finding repeated patterns, detecting outliers, and effectively …

This paper is focused on real-time visual analytics for home-based rehabilitation dedicated
for brain stroke survivors. This research is at the intersection of three main domains: visual …

Detecting anomalies in time-varying multivariate data is crucial in various industries for the
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …

Time series data is a sequence of values recorded systematically over a period which are
mostly used for prediction, clustering, and analysis. The two essential features of a time …

Frequency-based decomposition of time series data is used in many visualization
applications. Most of these decomposition methods (such as Fourier transform or singular …

The assessment of patient well-being is highly relevant for the early detection of diseases,
for assessing the risks of therapies, or for evaluating therapy outcomes. The knowledge to …

The characterization and abstraction of large multivariate time series data often poses
challenges with respect to effectiveness or efficiency. Using the example of human motion …

For the automatic segmentation of multivariate time series domain experts at first need to
consider a huge space of alternative configurations of algorithms and parameters. We …

Segmenting biologging time series of animals on multiple temporal scales is an essential
step that requires complex techniques with careful parameterization and possibly cross …

Choosing appropriate time series segmentation algorithms and relevant parameter values is
a challenging problem. In order to choose meaningful candidates it is important that different …