Predicting China's Crude Oil Futures Prices: A Strategic Comparison of Random Forest and Time Series Models

This article examines the critical roles of Random Forest (RF) and Time Series (TS) models in forecasting China's crude oil futures prices, providing a comprehensive comparison of their predictive capabilities. The study employs ARIMA and SARIMA models, known for their proficiency in capturing data trends and seasonality, to harness the temporal aspects of oil price movements. In contrast, the RF model is recognized for its robustness in handling complex datasets, offering a nuanced approach to non-linear relationships and variable interactions. The analysis reveals that the ARIMA (0,1,4) model outperforms the (1,1,0) model in terms of prediction error and statistical fitting. However, the RF model's strength lies in its precision and flexibility, particularly in responding to market fluctuations. The paper concludes with the insight that ARIMA models are more suitable for long-term strategic planning due to their stability, whereas RF models excel in short-term forecasting and high-accuracy prediction scenarios. These findings are invaluable for market participants, offering them data-driven strategies to optimize their decision-making processes in the volatile oil futures market.