  In this paper we survey the most recent advances in supervised machine
learning and high-dimensional models for time series forecasting. We consider
both linear and nonlinear alternatives. Among the linear methods we pay special
attention to penalized regressions and ensemble of models. The nonlinear
methods considered in the paper include shallow and deep neural networks, in
their feed-forward and recurrent versions, and tree-based methods, such as
random forests and boosted trees. We also consider ensemble and hybrid models
by combining ingredients from different alternatives. Tests for superior
predictive ability are briefly reviewed. Finally, we discuss application of
machine learning in economics and finance and provide an illustration with
high-frequency financial data.
