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Machine Learning Advances for Time Series Forecasting

Ricardo P. Masini, Marcelo C. Medeiros and Eduardo F. Mendes

Papers from arXiv.org

Abstract: 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.

Date: 2020-12, Revised 2021-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets and nep-for
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