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 …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

This paper presents a method for creating machine learning models, specifically a gradient
boosting model and a random forest model, to forecast real GDP growth. This study focuses …

Summary We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by
adding the how. The current forecasting literature has focused on matching specific …

In this paper we present and describe a large quarterly frequency, macroeconomic
database. The data provided are closely modeled to that used in Stock and Watson (2012a) …

This paper examines several ways to extract timely economic signals from newspaper text
and shows that such information can materially improve forecasts of macroeconomic …

Understanding and predicting power consumption behavior helps estimate costs, seek
actions to save energy and plan affirmative actions that raise people's awareness …

The consensus in the literature on providing accurate inflation forecasts underlines the
importance of precise nowcasts. In this paper, we focus on this issue by employing a unique …

We present a hierarchical architecture based on recurrent neural networks for predicting
disaggregated inflation components of the Consumer Price Index (CPI). While the majority of …

Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive,
we perform an in depth real‐time out‐of‐sample forecasting comparison study with one of …