Forecasting China’s Crude Oil Futures Price by Recurrent Neural Network Method Based on Unconstrained Transformation

This study examines whether a structure-preserving representation of the joint Open-High-Low-Close (OHLC) vector provides coherent forecasts for China’s INE crude-oil futures and how seven benchmark models compare within that representation. The sample contains 1547 trading days from the contract launch to 8 January 2025. Under the originally reported 80:20 setting, GRU has the smallest aggregate MAPE (0.7825%), MAE (4.6774), and RMSE (7.2957), together with the largest interval-overlap Success Ratio (0.5629). These figures establish a numerical ranking only: the archived materials do not verify the exact temporal split, training-only preprocessing, component-specific errors, or price-limit-event robustness. The economic section therefore contains only illustrative Close-to-Close and Open-to-Close mappings and does not claim executable profitability. The defensible contribution is the structure-preserving OHLC framework and a bounded numerical comparison.