  This paper presents a new fuel cycle benchmarking analysis methodology by
coupling Gaussian process regression, a popular technique in Machine Learning,
to dynamic time warping, a mechanism widely used in speech recognition.
Together they generate figures-of-merit that are applicable to any time series
metric that a benchmark may study. The figures-of-merit account for uncertainty
in the metric itself, utilize information across the whole time domain, and do
not require that the simulators use a common time grid. Here, a distance
measure is defined that can be used to compare the performance of each
simulator for a given metric. Additionally, a contribution measure is derived
from the distance measure that can be used to rank order the importance of fuel
cycle metrics. Lastly, this paper warns against using standard signal
processing techniques for error reduction. This is because it is found that
error reduction is better handled by the Gaussian process regression itself.
