  Since the introduction and the public availability of the \textsc{ucr} time
series benchmark data sets, numerous Time Series Classification (TSC) methods
has been designed, evaluated and compared to each others. We suggest a critical
view of TSC performance evaluation protocols put in place in recent TSC
literature. The main goal of this `position' paper is to stimulate discussion
and reflexion about performance evaluation in TSC literature.

Machine Learning;Machine Learning