  The study of natural and human-made processes often results in long sequences
of temporally-ordered values, aka time series (TS). Such processes often
consist of multiple states, e.g. operating modes of a machine, such that state
changes in the observed processes result in changes in the distribution of
shape of the measured values. Time series segmentation (TSS) tries to find such
changes in TS post-hoc to deduce changes in the data-generating process. TSS is
typically approached as an unsupervised learning problem aiming at the
identification of segments distinguishable by some statistical property.
Current algorithms for TSS require domain-dependent hyper-parameters to be set
by the user, make assumptions about the TS value distribution or the types of
detectable changes which limits their applicability. Common hyperparameters are
the measure of segment homogeneity and the number of change points, which are
particularly hard to tune for each data set. We present ClaSP, a novel, highly
accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP
hierarchically splits a TS into two parts. A change point is determined by
training a binary TS classifier for each possible split point and selecting the
one split that is best at identifying subsequences to be from either of the
partitions. ClaSP learns its main two model-parameters from the data using two
novel bespoke algorithms. In our experimental evaluation using a benchmark of
107 data sets, we show that ClaSP outperforms the state of the art in terms of
accuracy and is fast and scalable. Furthermore, we highlight properties of
ClaSP using several real-world case studies.
