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 …

In recent decades, Technology and Innovation (TI) have shown tremendous potential for
improving agricultural productivity and environmental sustainability. However, the adoption …

Exposure to environmental chemicals has been linked to an increased risk of pregnancy-
induced hypertension (PIH). This prospective cohort study examined the associations …

We propose a procedure of iterating the HP filter to produce a smarter smoothing device,
called the boosted HP (bHP) filter, based on L2‐boosting in machine learning. Limit theory …

The ability to predict urban floods is crucial for reducing potential losses. Previous studies
suggest that a multimodel combination is an effective way to improve the prediction …

Precise estimation of crop yield is crucial for ensuring food security, managing the supply
chain, optimally utilizing resources, promoting economic growth, enhancing climate …

Background The risk of gastric cancer after Helicobacter pylori (H. pylori) eradication
remains unknown. Aim To evaluate the performances of seven different machine learning …

In a low-dimensional linear regression setup, considering linear transformations/
combinations of predictors does not alter predictions. However, when the forecasting …

Return predictability has been one of the central research questions in finance for many
decades. This paper proposes a predictive regression with multiple structural changes to …

This study explores the diurnal variations and influencing factors of PM 2.5, NO 2, and ozone
concentrations in educational buildings. Utilizing an integrated system of indoor and outdoor …