Machine learning advances for time series forecasting

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