Visual-Interactive Labeling of Multivariate Time Series to Support Semi-Supervised Machine Learning

The labeling of multivariate time series is an essential requirement of data-centric decisionmaking processes in many time-oriented application domains. The basic idea of labeling is to assign (semantic) meaning to specific sections or time steps of the time series and to the time series as a whole, accordingly. Hence, weather phenomena can be characterized, EEG signals can be studied, or movement patterns can be marked in sensor data. In the context of this work a visual-interactive labeling tool was developed that allows nonexpert users to assign semantic meaning to any multivariate time series in an effective and efficient way. Enabling experts as well as non-experts to label multivariate time series in a visual-interactive way has never been proposed in the information visualization and visual analytics research communities before. This thesis combines active learning methods, a visual analytics approach, and novel visual-interactive interfaces to achieve an intuitive data exploration and labeling process for users. Visual guidance based on data analysis and model-based predictions empowers users to select and label meaningful instances from the time series. This user-side selection and labeling task can be taken over by an automated model or data-based process. Visual representations of labeling quality and novel interfaces allow for additional user-side refinement.