# Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods

## TL;DR

This paper shows that energy uncertainty affects crude oil prices, and machine learning models using this uncertainty index predict prices better than traditional methods.

## Contribution

The study introduces the Energy-Related Uncertainty Index (EUI) as a novel predictor in crude oil price forecasting using machine learning.

## Key findings

- The Energy-Related Uncertainty Index (EUI) significantly impacts crude oil price predictions.
- Machine learning models with EUI outperform linear regression in terms of prediction accuracy.
- Random Forest excels in short-term forecasts, while Attention-LSTM is better for long-term predictions when using EUI.

## Abstract

Energy related uncertainty has significant influence on crude oil market. To explore the influence, this paper investigates the predictive ability of the Energy-Related Uncertainty Index (EUI), over and above standard macroeconomic predictors, in forecasting crude oil prices using an array of machine learning methods. We find that EUI has a significant impact on crude oil prices. Moreover, machine learning methods combined with EUI performed better than the linear regression method due to a lower rate of prediction errors. Among these methods, the Random Forest (RF) model with EUI performs better in the short term, while the Attention-enhanced Long Short-Term Memory (Attention-LSTM) model with EUI has more substantial predictive power in the long term. These empirical results pass a series of robustness tests. Our findings have important implications for both regulators and investors in the crude oil market.