Machine Learning Advances for Time Series Forecasting

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)76-111
Number of pages36
JournalJournal of Economic Surveys
Issue number1
StateAccepted/In press - 2021
Externally publishedYes


  • bagging
  • boosting
  • deep learning
  • forecasting
  • machine learning
  • neural networks
  • nonlinear models
  • penalized regressions
  • random forests
  • regression trees
  • regularization
  • sieve approximation
  • statistical learning theory

ASJC Scopus subject areas

  • Economics and Econometrics


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