  Scientific fields such as insider-threat detection and highway-safety
planning often lack sufficient amounts of time-series data to estimate
statistical models for the purpose of scientific discovery. Moreover, the
available limited data are quite noisy. This presents a major challenge when
estimating time-series models that are robust to overfitting and have
well-calibrated uncertainty estimates. Most of the current literature in these
fields involve visualizing the time-series for noticeable structure and hard
coding them into pre-specified parametric functions. This approach is
associated with two limitations. First, given that such trends may not be
easily noticeable in small data, it is difficult to explicitly incorporate
expressive structure into the models during formulation. Second, it is
difficult to know $\textit{a priori}$ the most appropriate functional form to
use. To address these limitations, a nonparametric Bayesian approach was
proposed to implicitly capture hidden structure from time series having limited
data. The proposed model, a Gaussian process with a spectral mixture kernel,
precludes the need to pre-specify a functional form and hard code trends, is
robust to overfitting and has well-calibrated uncertainty estimates.
