- Published
- 2021-07-01
- Venue
- Journal of Economic Surveys, vol. 37, no. 1, pp. 76-111
- Cited by
- 527
- Open access
- Yes, green
- DOI
- 10.1111/joes.12429
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.
Cite this paper
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- APA 7
Masini, R., Medeiros, M. C., & Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 37(1), 76–111. https://doi.org/10.1111/joes.12429
- MLA 9
Masini, Ricardo, et al. "Machine learning advances for time series forecasting." Journal of Economic Surveys, vol. 37, no. 1, 2021, pp. 76–111, https://doi.org/10.1111/joes.12429.
- Chicago
Masini, Ricardo, Marcelo Cunha Medeiros, and Eduardo F. Mendes. "Machine learning advances for time series forecasting." Journal of Economic Surveys 37, no. 1 (2021): 76–111. https://doi.org/10.1111/joes.12429.
- GOST
Masini R., Medeiros M. C., Mendes E. F. Machine learning advances for time series forecasting // Journal of Economic Surveys. — 2021. — Vol. 37, № 1. — P. 76–111. — URL: https://doi.org/10.1111/joes.12429
- BibTeX
@article{masini2021machine, title = {{Machine learning advances for time series forecasting}}, author = {Ricardo Masini and Marcelo Cunha Medeiros and Eduardo F. Mendes}, journal = {Journal of Economic Surveys}, year = {2021}, volume = {37}, number = {1}, pages = {76--111}, doi = {10.1111/joes.12429}, url = {https://doi.org/10.1111/joes.12429} }- RIS
TY - JOUR AU - Masini, Ricardo AU - Medeiros, Marcelo Cunha AU - Mendes, Eduardo F. TI - Machine learning advances for time series forecasting JO - Journal of Economic Surveys VL - 37 IS - 1 SP - 76 EP - 111 PY - 2021 DA - 2021/07/01 DO - 10.1111/joes.12429 UR - https://doi.org/10.1111/joes.12429 ER -