Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

As crude oil price is influenced by numerous factors, capturing its behavior precisely is quite
challenging, and thus leads to the difficulty of forecasting. In this study, a deep learning …

Forecasting crude oil price is a challenging task. Given the nonlinear and time-varying
characteristics of international crude oil prices, we propose a novel hybrid method to …

Artificial intelligent methods are being extensively used for oil price forecasting as an
alternate approach to conventional techniques. There has been a whole spectrum of …

An accurate prediction of crude oil prices over long future horizons is challenging and of
great interest to governments, enterprises, and investors. This paper proposes a revised …

This study aims to forecast oil prices using evolutionary techniques such as gene expression
programming (GEP) and artificial neural network (NN) models to predict oil prices over the …

This paper proposes a novel hybrid forecast model to forecast crude oil price on considering
the long memory, asymmetric, heavy-tail distribution, nonlinear and non-stationary …

Iran energy intensity is among the highest in the world, which has been increasing during
the recent decades, originating from the channels related to the efficiency and structural …

Based on the wavelet analysis approach, this paper firstly examines the dynamic
relationship between global economic activity (proxied by the Kilian economic index) and …

This paper investigates the time-varying correlation and the causal relationship between
crude oil spot and futures prices using a newly developed approach—wavelet coherency …