Arbeitspapier
Machine learning advances for time series forecasting
In this paper we survey the most recent advances in supervised machine learning and highdimensional 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 brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.
- Sprache
-
Englisch
- Erschienen in
-
Series: Texto para discussão ; No. 679
- Klassifikation
-
Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- Thema
-
Machine learning
statistical learning theory
penalized regressions
regularization
sieve approximation
nonlinear models
neural networks
deep learning
regression trees
random forests
boosting
bagging
forecasting
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Masini, Ricardo P.
Medeiros, Marcelo C.
Mendes, Eduardo F.
- Ereignis
-
Veröffentlichung
- (wer)
-
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
- (wo)
-
Rio de Janeiro
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Masini, Ricardo P.
- Medeiros, Marcelo C.
- Mendes, Eduardo F.
- Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
Entstanden
- 2020
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