  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.
