Machine learning advances for time series forecasting
RP Masini, MC Medeiros… - Journal of economic …, 2023 - Wiley Online Library
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 …
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …
Forecasting with machine learning methods
MC Medeiros - Econometrics with machine learning, 2022 - Springer
This chapter surveys the use of supervised Machine Learning (ML) models to forecast time-
series data. Our focus is on covariance stationary dependent data when a large set of …
series data. Our focus is on covariance stationary dependent data when a large set of …
Penalized averaging of parametric and non-parametric quantile forecasts
JG De Gooijer, D Zerom - Journal of Time Series Econometrics, 2020 - degruyter.com
We propose a hybrid penalized averaging for combining parametric and non-parametric
quantile forecasts when faced with a large number of predictors. This approach goes …
quantile forecasts when faced with a large number of predictors. This approach goes …