  Time series data play an important role in many applications and their
analysis reveals crucial information for understanding the underlying
processes. Among the many time series learning tasks of great importance, we
here focus on semi-supervised learning which benefits of a graph representation
of the data. Two main aspects are involved in this task: A suitable distance
measure to evaluate the similarities between time series, and a learning method
to make predictions based on these distances. However, the relationship between
the two aspects has never been studied systematically. We describe four
different distance measures, including (Soft) DTW and Matrix Profile, as well
as four successful semi-supervised learning methods, including the graph Allen-
Cahn method and a Graph Convolutional Neural Network. We then compare the
performance of the algorithms on standard data sets. Our findings show that all
measures and methods vary strongly in accuracy between data sets and that there
is no clear best combination to employ in all cases.
