Machine learning advances for time series forecasting
Ricardo Masini, Marcelo Cunha Medeiros, Eduardo F. Mendes
2021 · Journal of Economic Surveys · 523 citations
Abstract 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 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 feedforward 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…
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