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 …
Machine learning time series regressions with an application to nowcasting
This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …
ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors
MC Medeiros, EF Mendes - Journal of Econometrics, 2016 - Elsevier
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-
dimensional, linear time-series models. The adaLASSO is a one-step implementation of the …
dimensional, linear time-series models. The adaLASSO is a one-step implementation of the …
Regularized estimation of high‐dimensional vector autoregressions with weakly dependent innovations
RP Masini, MC Medeiros… - Journal of Time Series …, 2022 - Wiley Online Library
There has been considerable advance in understanding the properties of sparse
regularization procedures in high‐dimensional models. In time series context, it is mostly …
regularization procedures in high‐dimensional models. In time series context, it is mostly …
Predicting inflation component drivers in Nigeria: a stacked ensemble approach
Our study examined the disaggregation of inflation components in Nigeria using the stacked
ensemble approach, a machine learning algorithm capable of compensating the weakness …
ensemble approach, a machine learning algorithm capable of compensating the weakness …
High dimensional time series regression models: Applications to statistical learning methods
C Katsouris - arXiv preprint arXiv:2308.16192, 2023 - arxiv.org
These lecture notes provide an overview of existing methodologies and recent
developments for estimation and inference with high dimensional time series regression …
developments for estimation and inference with high dimensional time series regression …
On the adaptive Lasso estimator of AR (p) time series with applications to INAR (p) and Hawkes processes
D De Canditiis, GL Torrisi - Journal of Statistical Planning and Inference, 2024 - Elsevier
We investigate the consistency and the rate of convergence of the adaptive Lasso estimator
for the parameters of linear AR (p) time series with a white noise which is a strictly stationary …
for the parameters of linear AR (p) time series with a white noise which is a strictly stationary …
Tracking ECB's Communication: Perspectives and implications for financial markets
R Fortes, T Le Guenedal - Available at SSRN 3791244, 2020 - papers.ssrn.com
This article assesses the communication of the European Central Bank (ECB) using Natural
Language Processing (NLP) techniques. We show the evolution of discourse over time and …
Language Processing (NLP) techniques. We show the evolution of discourse over time and …
L_1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations
MC Medeiros, EF Mendes - Available at SSRN 2626507, 2015 - papers.ssrn.com
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-
dimensional, linear time-series models. We assume that both the number of covariates in the …
dimensional, linear time-series models. We assume that both the number of covariates in the …
Lasso and Ridge for GARCH-X Models
This paper examines the efficacy of the least absolute shrinkage and selection operator
(Lasso) and Ridge algorithms in improving the volatility forecasting of the Generalized …
(Lasso) and Ridge algorithms in improving the volatility forecasting of the Generalized …