  Rapid technological innovation threatens to leave much of the global
workforce behind. Today's economy juxtaposes white-hot demand for skilled labor
against stagnant employment prospects for workers unprepared to participate in
a digital economy. It is a moment of peril and opportunity for every country,
with outcomes measured in long-term capital allocation and the life
satisfaction of billions of workers. To meet the moment, governments and
markets must find ways to quicken the rate at which the supply of skills reacts
to changes in demand. More fully and quickly understanding labor market
intelligence is one route. In this work, we explore the utility of time series
forecasts to enhance the value of skill demand data gathered from online job
advertisements. This paper presents a pipeline which makes one-shot multi-step
forecasts into the future using a decade of monthly skill demand observations
based on a set of recurrent neural network methods. We compare the performance
of a multivariate model versus a univariate one, analyze how correlation
between skills can influence multivariate model results, and present
predictions of demand for a selection of skills practiced by workers in the
information technology industry.
