  The sparsity-ranked lasso (SRL) has been developed for model selection and
estimation in the presence of interactions and polynomials. The main tenet of
the SRL is that an algorithm should be more skeptical of higher-order
polynomials and interactions *a priori* compared to main effects, and hence the
inclusion of these more complex terms should require a higher level of
evidence. In time series, the same idea of ranked prior skepticism can be
applied to the possibly seasonal autoregressive (AR) structure of the series
during the model fitting process, becoming especially useful in settings with
uncertain or multiple modes of seasonality. The SRL can naturally incorporate
exogenous variables, with streamlined options for inference and/or feature
selection. The fitting process is quick even for large series with a
high-dimensional feature set. In this work, we discuss both the formulation of
this procedure and the software we have developed for its implementation via
the **fastTS** R package. We explore the performance of our SRL-based approach
in a novel application involving the autoregressive modeling of hourly
emergency room arrivals at the University of Iowa Hospitals and Clinics. We
find that the SRL is considerably faster than its competitors, while producing
more accurate predictions.
