  sktime is an open source, Python based, sklearn compatible toolkit for time
series analysis developed by researchers at the University of East Anglia
(UEA), University College London and the Alan Turing Institute. A key initial
goal for sktime was to provide time series classification functionality
equivalent to that available in a related java package, tsml, also developed at
UEA. We describe the implementation of six such classifiers in sktime and
compare them to their tsml equivalents. We demonstrate correctness through
equivalence of accuracy on a range of standard test problems and compare the
build time of the different implementations. We find that there is significant
difference in accuracy on only one of the six algorithms we look at (Proximity
Forest). This difference is causing us some pain in debugging. We found a much
wider range of difference in efficiency. Again, this was not unexpected, but it
does highlight ways both toolkits could be improved.
