This study proposes to use a hybrid ensemble learning approach to improve the prediction
efficiency of crude oil prices. It combines the Long Short-Term Memory (LSTM) with factors …

The objective of this paper is to study the oil price forecasting using parsimonious models
that provide the best possible match using the fewest variables or parameters and to …

Predicting unrefined petroleum prices is crucial for decision-makers, financiers, and
academics in the energy industry. This paper presents an economic system-based …

Crude oil is a crucial global commodity. With an increasing population and expanding
industries, crude oil prices are highly erratic and subject to high uncertainty. The slightest …

Crude oil prices are susceptible to a variety of elements, including military, political,
financial, and diplomatic forces, resulting in price fluctuations that are random, abrupt, and …

The importance of crude oil is undeniable nowadays. Therefore, forecasting its price is vital
for many industries and countries to specify their strategies. In this regard, many studies …

In recent years, the crude oil market has entered a new period of development and the core
influence factors of crude oil have also been a change. Thus, we develop a new research …

The crude oil price (COP) has substantial implications on world economy, as it impacts
industries ranging from transportation to manufacturing. Given the volatile nature of COP …

Since the commodity and financial attributes of crude oil will have a long‐term or short‐term
impact on crude oil prices, we propose a de‐dimension machine learning model approach …

The crude oil market is unstable, and its price is highly volatile. Due to the Covid-19
pandemic, the price of crude oils goes up and down in a short period of time. Future plans …