A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hierarchical RF-XGBoost model for precise agricultural sales forecasting, significantly reducing food waste by improving demand prediction. The model enhances supply chain efficiency from farm to table.
Area Of Science
- Agricultural Economics
- Data Science
- Supply Chain Management
Background
- Short-cycle agricultural product sales forecasting is crucial for minimizing food waste by aligning supply with demand.
- Forecasting accuracy is challenged by volatile and discontinuous sales data due to uncertain factors.
- Existing models often struggle with the inherent complexities of agricultural market dynamics.
Purpose Of The Study
- To develop and evaluate a novel hierarchical prediction model for enhanced short-term agricultural product sales forecasting.
- To improve demand prediction accuracy, thereby reducing food waste and optimizing the agricultural supply chain.
- To address the volatility and discontinuity in sales data through an advanced modeling approach.
Main Methods
- A hierarchical model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) was developed.
- The first layer uses RF with Grey Relation Analysis (GRA) for initial predictions and residual extraction.
- The second layer employs XGBoost on residual clustering features for refined forecasting.
Main Results
- The proposed RF-XGBoost model demonstrated superior performance compared to standalone RF and XGBoost.
- Achieved a 10% and 12% reduction in Mean Absolute Percentage Error (MAPE) over RF and XGBoost, respectively.
- Showcased a 22% and 24% increase in the coefficient of determination (R²), indicating higher prediction accuracy.
Conclusions
- The hierarchical RF-XGBoost model significantly enhances the precision of short-term agricultural sales forecasting.
- The model's effectiveness was validated across diverse agricultural products, demonstrating broad applicability.
- Implementation offers substantial benefits for supply chain optimization and food waste reduction.
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