Data Science Chair

    Our paper "Semi-unsupervised Learning forTime Series Classification" got accepted @ MILETS (KDD22)


    Our workshop paper "Semi-unsupervised Learning forTime Series Classification" got accepted @ MILETS (8th SIGKDD International Workshop on Mining and Learning from Time Series -- Deep Forecasting: Models, Interpretability, and Applications) at KDD22.

    In this work we analyze the application of semi-unsupervised learning for raw time series classification tasks.


    Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data.

    In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised).

    We demonstrate the efficacy of our approach with established time series classification datasets from different domains.