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 …

Regression models are a form of supervised learning methods that are important for
machine learning, statistics, and general data science. Despite the fact that classical …

Inflation forecasting is an important but difficult task. Here, we explore advances in machine
learning (ML) methods and the availability of new datasets to forecast US inflation. Despite …

This study investigates the effectiveness of sparse regression models with their diverse
specifications and the impulse indicator saturation (IIS) method in forecasting crude oil …

We use regularized machine learning models to forecast Brazilian power electricity
consumption for short and medium terms. We compare our models to benchmark …

Abstract Machine learning techniques have been used frequently for volatility forecasting.
However, previous studies have built these hybrid models in a form of a first-order GARCH …

The problem of inflation is crucial for ensuring sustainable economic growth of the country.
In the broadest sense, the economic dimension of sustainable development represents the …

Forecasting inflation accurately in a data-rich environment is a challenging task and an
active research field which still contains various unanswered methodological questions. One …

This paper combines the discrete wavelet transform with support vector regression for
forecasting gold-price dynamics. The advantages of this approach are investigated using a …

The purpose of this paper is to explore machine learning techniques to forecast the oil price.
In the era of big data, we investigate whether new automated tools can improve over …