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 and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The n...

Full description

Saved in:
Bibliographic Details
Published inJournal of economic surveys Vol. 37; no. 1; pp. 76 - 111
Main Authors Masini, Ricardo P., Medeiros, Marcelo C., Mendes, Eduardo F.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.02.2023
Subjects
bagging
boosting
deep learning
Finance
forecasting
Forests
Machine learning
Neural networks
nonlinear models
penalized regressions
Predictions
random forests
Recurrent
regression trees
regularization
sieve approximation
statistical learning theory
Time series
Trees
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0950-0804
1467-6419
DOI:10.1111/joes.12429