Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm
The time series of agricultural prices exhibit brevity and considerable volatility. Considering that traditional time series models and machine learning models are facing challenges in making predictions with high accuracy and robustness, this paper proposes a Light gradient boosting machine model b...
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/71 |
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| author | Nana Zhang Qi An Shuai Zhang Huanhuan Ma |
| author_facet | Nana Zhang Qi An Shuai Zhang Huanhuan Ma |
| author_sort | Nana Zhang |
| collection | DOAJ |
| description | The time series of agricultural prices exhibit brevity and considerable volatility. Considering that traditional time series models and machine learning models are facing challenges in making predictions with high accuracy and robustness, this paper proposes a Light gradient boosting machine model based on the boosting ensemble learning algorithm to predict prices for three representative types of fresh agricultural products (bananas, beef, crucian carp). The prediction performance of the Light gradient boosting machine model is evaluated by comparing it against multiple benchmark models (ARIMA, decision tree, random forest, support vector machine, XGBoost, and artificial neural network) in terms of accuracy, generalizability, and robustness on different datasets and under different time windows. Among these models, the Light gradient boosting machine model is shown to have the highest prediction accuracy and the most stable performance across three different datasets under both long-term and short-term time windows. As the time window length increases, the Light gradient boosting machine model becomes more advantageous for effectively reducing error fluctuation, demonstrating better robustness. Consequently, the model proposed in this paper holds significant potential for forecasting fresh agricultural product prices, thereby facilitating the advancement of precision and sustainable farming practices. |
| format | Article |
| id | doaj-art-a4b08e30189f4ea690a64f6719af0fcf |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a4b08e30189f4ea690a64f6719af0fcf2025-01-10T13:18:09ZengMDPI AGMathematics2227-73902024-12-011317110.3390/math13010071Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble AlgorithmNana Zhang0Qi An1Shuai Zhang2Huanhuan Ma3College of Economics & Management, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, ChinaSchool of Engineering, The Open University of China, Beijing 100039, ChinaCollege of Business Administration, Capital University of Economics and Business, Beijing 100070, ChinaCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaThe time series of agricultural prices exhibit brevity and considerable volatility. Considering that traditional time series models and machine learning models are facing challenges in making predictions with high accuracy and robustness, this paper proposes a Light gradient boosting machine model based on the boosting ensemble learning algorithm to predict prices for three representative types of fresh agricultural products (bananas, beef, crucian carp). The prediction performance of the Light gradient boosting machine model is evaluated by comparing it against multiple benchmark models (ARIMA, decision tree, random forest, support vector machine, XGBoost, and artificial neural network) in terms of accuracy, generalizability, and robustness on different datasets and under different time windows. Among these models, the Light gradient boosting machine model is shown to have the highest prediction accuracy and the most stable performance across three different datasets under both long-term and short-term time windows. As the time window length increases, the Light gradient boosting machine model becomes more advantageous for effectively reducing error fluctuation, demonstrating better robustness. Consequently, the model proposed in this paper holds significant potential for forecasting fresh agricultural product prices, thereby facilitating the advancement of precision and sustainable farming practices.https://www.mdpi.com/2227-7390/13/1/71boosting ensemble learning algorithmlight gradient boosting machinefresh agricultural productsprice predictions |
| spellingShingle | Nana Zhang Qi An Shuai Zhang Huanhuan Ma Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm Mathematics boosting ensemble learning algorithm light gradient boosting machine fresh agricultural products price predictions |
| title | Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm |
| title_full | Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm |
| title_fullStr | Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm |
| title_full_unstemmed | Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm |
| title_short | Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm |
| title_sort | price prediction for fresh agricultural products based on a boosting ensemble algorithm |
| topic | boosting ensemble learning algorithm light gradient boosting machine fresh agricultural products price predictions |
| url | https://www.mdpi.com/2227-7390/13/1/71 |
| work_keys_str_mv | AT nanazhang pricepredictionforfreshagriculturalproductsbasedonaboostingensemblealgorithm AT qian pricepredictionforfreshagriculturalproductsbasedonaboostingensemblealgorithm AT shuaizhang pricepredictionforfreshagriculturalproductsbasedonaboostingensemblealgorithm AT huanhuanma pricepredictionforfreshagriculturalproductsbasedonaboostingensemblealgorithm |