<![CDATA[Accurate short-term forecasting of Crude Palm Oil (CPO) spot prices is critical for managing financial risk and stabilizing supply chains in volatile agricultural commodity markets. However, high non-linearity, temporal volatility, and sensitivity to market shocks present significant modeling challenges for traditional econometric tools. To address these issues, this study aims to develop an optimized machine learning framework that evaluates the comparative predictive performance of kernel-based margin regression against decision-tree ensembles for daily CPO spot price forecasting. Utilizing historical Malaysian CPO spot price data (2015–2024), the proposed solution combines multi-temporal feature engineering (autoregressive lags and simple moving averages) with a leak-free TimeSeriesSplit cross-validation and systematic grid-search hyperparameter tuning. Out-of-sample evaluation demonstrates that the tuned Support Vector Regression (SVR) model achieves superior predictive accuracy, registering an RMSE of 24.2191 USD, an MAE of 15.8937 USD, and a MAPE of 1.6940%. The SVR architecture significantly outperforms the optimized Random Forest (RF) Regressor (RMSE = 35.2395 USD, MAE = 23.8164 USD, MAPE = 2.5151%), yielding a 31.27% error reduction in RMSE. Feature importance analysis establishes that the 1-day lag (lag_1) contributes 66.75% of total split impurity reduction. Furthermore, SVR maintains homoscedastic stability during extreme late-2024 price surges exceeding 1113.00 USD, where Random Forest exhibits extrapolation truncation. This study contributes a validated, operationally sound decision-support blueprint for physical commodity risk management and clarifies algorithm selection under structural market shocks.]]>
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