  In this work, we present a simple interpolation methodology for spectroscopic
time series, based on conventional interpolation techniques (random forests)
implemented in widely-available libraries. We demonstrate that our existing
library of simulations is sufficient for training, producing interpolated
spectra that respond sensitively to varied ejecta parameter, post-merger time,
and viewing angle inputs. We compare our interpolated spectra to the AT2017gfo
spectral data, and find parameters similar to our previous inferences using
broadband light curves. However, the spectral observations have significant
systematic short-wavelength residuals relative to our models, which we cannot
explain within our existing framework. Similar to previous studies, we argue
that an additional blue component is required. We consider a radioactive
heating source as a third component characterized by light, slow-moving,
lanthanide-free ejecta with $M_{\rm th} = 0.003~M_\odot$, $v_{\rm th} = 0.05$c,
and $\kappa_{\rm th} = 1$ cm$^2$/g. When included as part of our radiative
transfer simulations, our choice of third component reprocesses blue photons
into lower energies, having the opposite effect and further accentuating the
blue-underluminosity disparity in our simulations. As such, we are unable to
overcome short-wavelength deficits at later times using an additional
radioactive heating component, indicating the need for a more sophisticated
modeling treatment.
