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基于LSTM模型的蔬菜价格短期预测-来自北京蔬菜数据的证据

Qi Zhang¹, Weijia Yang², Anping Zhao²

¹School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China.

|July 11, 2024

概括

准确的蔬菜价格预测对于市场稳定至关重要. 一个LSTM模型比传统方法准确率高出5%以上,帮助种植者和决策者.

科学领域:

农业经济学农业经济学
时间序列分析时间序列分析
机器学习机器学习

背景情况:

准确的蔬菜价格预测对于农业部门的经济稳定至关重要.
蔬菜定价的非线性模式挑战了传统的时间序列方法.
蔬菜行业是国民经济的重要组成部分.

研究的目的:

开发和评估一个长短期记忆 (LSTM) 模型,用于准确地预测蔬菜价格.
将LSTM模型的性能与其他机器学习技术进行比较.
为蔬菜市场提供知情决策的工具.

主要方法:

利用来自北京七个批发市场的六种蔬菜类型的每日平均价格数据 (2009-2023年).
训练并测试了一个长期短期记忆 (LSTM) 神经网络模型.
将LSTM性能与基于卷积神经网络 (CNN) 的时间序列预测和其他机器学习方法进行比较.

主要成果:

在测试数据集上,LSTM模型表现出了卓越的性能,R2得分为0.958,MAE为0.143.
与传统机器学习同行相比,实现了超过5%的更高预测准确度.
在不同的蔬菜类别中表现出强大的预测性能和概括能力.

结论:

该LSTM模型为蔬菜价格预测提供了一个高度准确和可靠的方法.
这种预测工具可以提高市场透明度,优化供应链,并支持可持续发展.
应用包括帮助种植者,消费者和决策者在市场决策中.

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