基于数据的气候影响对番茄和果价格的分析,使用机器学习
Sunghyun Yoon1, Tae-Hwa Kim2, Dong Sub Kim3
1Department of Artificial Intelligence, Kongju National University, Cheonan, 31080, Republic of Korea.
Heliyon
|January 21, 2025
概括
机器学习通过考虑环境因素和时间滞后,准确地预测水果价格. 这种方法揭示了气候变量如何影响农业经济,帮助气候变化适应.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 关于将机器学习应用于农产品价格预测的研究有限.
- 假设环境因素通过作物生长间接影响水果生产和价格.
- 了解环境变化和价格波动之间的时间动态对于农业市场至关重要.
研究的目的:
- 评估使用环境数据预测番茄和果价格的准确性.
- 量化单个环境因素对水果价格的影响.
- 探索时间滞后在环境变量与水果价格之间的关系中的作用.
主要方法:
- 利用机器学习技术,特别是长短期记忆 (LSTM) 网络,用于价格预测.
- 模拟了环境因素与水果价格之间的数据驱动关系,并纳入了可变时间延迟.
- 采用沙普利添加物解释 (SHAP) 来确定每个环境因素的重要性.
主要成果:
- 该研究通过结合环境数据成功预测了水果价格,并确定了最佳的时间延迟.
- 包括时间延迟在内,显著提高了价格预测的准确性.
- SHAP分析提供了关于特定环境因素对水果价格的影响的见解.
结论:
- 机器学习模型,特别是LSTM,通过考虑环境变量和时间滞后,可以有效预测农产品价格.
- 识别和整合适当的时间延迟可以提高预测的准确性.
- 这种数据驱动的方法为农业提供了有价值的决策支持,特别是在气候变化的背景下.
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