Predictive Modeling of Peanut Oil Prices Utilizing a Gaussian Process Regression-Based Machine Learning Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate anticipation of fluctuations in commodity valuations is critical for diverse stakeholders, encompassing policymakers, investors, and supply chain entities, to ensure informed decision-making within volatile markets. As a staple edible oil, peanut oil exhibits pronounced price volatility, necessitating robust predictive frameworks to mitigate economic risks. This study leverages a decade-long weekly wholesale price index data set (January 1, 2010–January 10, 2020) to model price dynamics within the Chinese agricultural sector. A Gaussian process regression (GPR) methodology is implemented, integrating Bayesian optimization for hyperparameter tuning and [Formula: see text]-fold cross-validation to systematically evaluate diverse kernel functions and basis configurations. Empirical validation reveals the model’s predictive efficacy, achieving a relative root mean square error (RRMSE) of 0.6823% during the out-of-sample evaluation phase (January 5, 2018–January 10, 2020), underscoring its reliability in capturing nonlinear price trends. The proposed machine learning framework not only serves as an autonomous tool for generating technical price projections but also can complement ensemble forecasting systems by synthesizing insights with econometric or fundamental models. Forecast results here could enhance the granularity of commodity market analyses, offering policymakers and analysts multidimensional perspectives for strategic planning and policy research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it