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 …

This article introduces structured machine learning regressions for high-dimensional time
series data potentially sampled at different frequencies. The sparse-group LASSO estimator …

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 …

There has been considerable advance in understanding the properties of sparse
regularization procedures in high‐dimensional models. In time series context, it is mostly …

Our study examined the disaggregation of inflation components in Nigeria using the stacked
ensemble approach, a machine learning algorithm capable of compensating the weakness …

These lecture notes provide an overview of existing methodologies and recent
developments for estimation and inference with high dimensional time series regression …

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 …

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 …

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 …

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 …