  We present an approach for computationally efficient dynamic time warping
(DTW) and clustering of time-series data. The method frames the dynamic warping
of time series datasets as an optimisation problem solved using dynamic
programming, and then clusters time series data by solving a second
optimisation problem using mixed-integer programming (MIP). There is also an
option to use k-medoids clustering for increased speed, when a certificate for
global optimality is not essential. The improved efficiency of our approach is
due to task-level parallelisation of the clustering alongside DTW. Our approach
was tested using the UCR Time Series Archive, and was found to be, on average,
33% faster than the next fastest option when using the same clustering method.
This increases to 64% faster when considering only larger datasets (with more
than 1000 time series). The MIP clustering is most effective on small numbers
of longer time series, because the DTW computation is faster than other
approaches, but the clustering problem becomes increasingly computationally
expensive as the number of time series to be clustered increases.
