  It is well understood that Dynamic Time Warping (DTW) is effective in
revealing similarities between time series that do not align perfectly. In this
paper, we illustrate this on spectroscopy time-series data. We show that DTW is
effective in improving accuracy on a regression task when only a single
wavelength is considered. When combined with k-Nearest Neighbour, DTW has the
added advantage that it can reveal similarities and differences between samples
at the level of the time-series. However, in the problem, we consider here data
is available across a spectrum of wavelengths. If aggregate statistics (means,
variances) are used across many wavelengths the benefits of DTW are no longer
apparent. We present this as another example of a situation where big data
trumps sophisticated models in Machine Learning.
