Machine Learning Versus Deep Learning: SVR and LSTM Models for WTI Price Forecasting

Abstract

This study compares Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) models for forecasting annual West Texas Intermediate (WTI) crude oil prices using data from 1981-2023, with projections to 2035. The US Dollar Index (DXY) is incorporated as an explanatory variable to capture exchange-rate effects in global oil markets. A walk-forward crossvalidation framework is employed, and forecasting performance is evaluated using MSE, RMSE, MAE, MAPE, and $\mathrm{R}^{2}$. Results reveal a moderate negative correlation between WTI prices and the DXY index. Forecast comparison tests, including the paired t-test, Wilcoxon signed-rank test, and Diebold-Mariano (DM) test, consistently show that SVR outperforms LSTM. Incorporating DXY further improves forecasting accuracy, particularly for SVR. The extended SVR model achieves the highest explanatory power $\left(\mathrm{R}^{2}=0.928\right)$, compared with the baseline SVR $\left(\mathrm{R}^{2}=0.912\right)$, baseline LSTM $\left(\mathrm{R}^{2}=0.726\right)$, and extended LSTM $\left(\mathrm{R}^{2}=0.781\right)$. These findings suggest that SVR augmented with macro-financial information provides a more suitable framework for medium-term energy and fiscal policy analysis.