Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

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 …

Concrete is a widely used construction material, and cement is its main constituent.
Production and utilization of cement severely affect the environment due to the emission of …

Whether stock prices are predictable has been the center of debate in academia. In this
paper, we propose a hybrid model that combines a deep learning approach with a sentiment …

This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …

Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk
management, and hedging strategies. Therefore, accurate prediction of volatility is critical …

In the last decades, the world's energy consumption has increased rapidly due to
fundamental changes in the industry and economy. In such terms, accurate demand …

The realized volatility (RV) financial time series is non-linear, volatile, and noisy. It is not
easy to accurately forecast RV with a single forecasting model. This paper adopts a hybrid …

Compressive and flexural strength are the crucial properties of a material. The strength of
recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate …

We inspect how accurate machine learning (ML) is at forecasting realized variance of the
Dow Jones Industrial Average index constituents. We compare several ML algorithms …