Price Prediction for Fresh Agricultural Products Based on a Boosting Ensemble Algorithm

Nana Zhang, Qi An, Shuai Zhang and Huanhuan Ma ()
Additional contact information
Nana Zhang: College of Economics & Management, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Qi An: School of Engineering, The Open University of China, Beijing 100039, China
Shuai Zhang: College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
Huanhuan Ma: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Mathematics, 2024, vol. 13, issue 1, 1-17

Abstract: 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.

Keywords: boosting ensemble learning algorithm; light gradient boosting machine; fresh agricultural products; price predictions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/1/71/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/1/71/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2024:i:1:p:71-:d:1555286

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().