  Early Time-Series Classification (ETSC) is the task of predicting the class
of incoming time-series by observing as few measurements as possible. Such
methods can be employed to obtain classification forecasts in many
time-critical applications. However, available techniques are not equally
suitable for every problem, since differentiations in the data characteristics
can impact algorithm performance in terms of earliness, accuracy, F1-score, and
training time. We evaluate six existing ETSC algorithms on publicly available
data, as well as on two newly introduced datasets originating from the life
sciences and maritime domains. Our goal is to provide a framework for the
evaluation and comparison of ETSC algorithms and to obtain intuition on how
such approaches perform on real-life applications. The presented framework may
also serve as a benchmark for new related techniques.
