A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting
Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address th...
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MDPI AG
2024-09-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/13/18/2936 |
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| author | Jiawen Li Binfan Lin Peixian Wang Yanmei Chen Xianxian Zeng Xin Liu Rongjun Chen |
| author_facet | Jiawen Li Binfan Lin Peixian Wang Yanmei Chen Xianxian Zeng Xin Liu Rongjun Chen |
| author_sort | Jiawen Li |
| collection | DOAJ |
| description | Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R<sup>2</sup>) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly. |
| format | Article |
| id | doaj-art-d8be14852c9a49f0853e0c9f3dffa62b |
| institution | OA Journals |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-d8be14852c9a49f0853e0c9f3dffa62b2025-08-20T01:55:31ZengMDPI AGFoods2304-81582024-09-011318293610.3390/foods13182936A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales ForecastingJiawen Li0Binfan Lin1Peixian Wang2Yanmei Chen3Xianxian Zeng4Xin Liu5Rongjun Chen6School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaDepartment of Electrical and Computer Engineering, University of Macau, Macau 999078, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaShort-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R<sup>2</sup>) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly.https://www.mdpi.com/2304-8158/13/18/2936RF-XGBoosthierarchical clusteringagricultural productsales forecastingfood waste reduction |
| spellingShingle | Jiawen Li Binfan Lin Peixian Wang Yanmei Chen Xianxian Zeng Xin Liu Rongjun Chen A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting Foods RF-XGBoost hierarchical clustering agricultural product sales forecasting food waste reduction |
| title | A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting |
| title_full | A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting |
| title_fullStr | A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting |
| title_full_unstemmed | A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting |
| title_short | A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting |
| title_sort | hierarchical rf xgboost model for short cycle agricultural product sales forecasting |
| topic | RF-XGBoost hierarchical clustering agricultural product sales forecasting food waste reduction |
| url | https://www.mdpi.com/2304-8158/13/18/2936 |
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