  There have recently been significant advances in the accuracy of algorithms
proposed for time series classification (TSC). However, a commonly asked
question by real world practitioners and data scientists less familiar with the
research topic, is whether the complexity of the algorithms considered state of
the art is really necessary. Many times the first approach suggested is a
simple pipeline of summary statistics or other time series feature extraction
approaches such as TSFresh, which in itself is a sensible question; in
publications on TSC algorithms generalised for multiple problem types, we
rarely see these approaches considered or compared against. We experiment with
basic feature extractors using vector based classifiers shown to be effective
with continuous attributes in current state-of-the-art time series classifiers.
We test these approaches on the UCR time series dataset archive, looking to see
if TSC literature has overlooked the effectiveness of these approaches. We find
that a pipeline of TSFresh followed by a rotation forest classifier, which we
name FreshPRINCE, performs best. It is not state of the art, but it is
significantly more accurate than nearest neighbour with dynamic time warping,
and represents a reasonable benchmark for future comparison.
