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Machine learning advances for time series forecasting
Authors:Ricardo P. Masini  Marcelo C. Medeiros  Eduardo F. Mendes
Affiliation:1. Center for Statistics and Machine Learning, Princeton University, USA

São Paulo School of Economics, Getulio Vargas Foundation, Brazil;2. Department of Economics, Pontifical Catholic University of Rio de Janeiro, Brazil;3. School of Applied Mathematics, Getulio Vargas Foundation, Brazil

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. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.
Keywords:bagging  boosting  deep learning  forecasting  machine learning  neural networks  nonlinear models  penalized regressions  random forests  regression trees  regularization  sieve approximation  statistical learning theory
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