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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 …
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
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
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 …
Tracking ECB's communication: Perspectives and Implications for Financial Markets
R Fortes, T Le Guenedal - 2020 - mpra.ub.uni-muenchen.de
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 …
10. Econometrics of machine learning methods in economic forecasting
Economic forecasting has traditionally relied on models estimated with the maximum
likelihood (MLE) approach. The limitations of the MLE are well known as eloquently …
likelihood (MLE) approach. The limitations of the MLE are well known as eloquently …
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 …
L_1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations
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 …
On the adaptive Lasso estimator of AR (p) time series with applications to INAR (p) and Hawkes processes
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 …