A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction
Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low...
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2025-05-01
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| author | Bo Li Yuanqiang Lian |
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| description | Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics. |
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| spelling | doaj-art-1a1fa325932541d6b079d9a8aa9bae9b2025-08-20T02:33:43ZengMDPI AGApplied Sciences2076-34172025-05-011510557510.3390/app15105575A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual CorrectionBo Li0Yuanqiang Lian1Business School, Yangzhou University, Yangzhou 225127, ChinaBusiness School, Yangzhou University, Yangzhou 225127, ChinaWholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics.https://www.mdpi.com/2076-3417/15/10/5575induced ordered weighted averaging operatormachine learningsparrow search algorithmresidual correctionagricultural product price forecast |
| spellingShingle | Bo Li Yuanqiang Lian A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction Applied Sciences induced ordered weighted averaging operator machine learning sparrow search algorithm residual correction agricultural product price forecast |
| title | A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction |
| title_full | A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction |
| title_fullStr | A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction |
| title_full_unstemmed | A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction |
| title_short | A Forecasting Approach for Wholesale Market Agricultural Product Prices Based on Combined Residual Correction |
| title_sort | forecasting approach for wholesale market agricultural product prices based on combined residual correction |
| topic | induced ordered weighted averaging operator machine learning sparrow search algorithm residual correction agricultural product price forecast |
| url | https://www.mdpi.com/2076-3417/15/10/5575 |
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