用于预测香价格的循环神经网络架构在印度古吉拉特邦
Prity Kumari1, Viniya Goswami2, Harshith N2
1College of Horticulture, Anand Agricultural University, Anand, Gujarat, India.
PloS one
|June 15, 2023
概括
循环神经网络 (RNN) 模型在印度准确预测香价格方面显著优于传统的统计和其他机器学习方法. 这一进步为园艺商品提供了更好的价格预测准确性.
科学领域:
- 农业经济学 农业经济学
- 机器学习应用 机器学习应用
- 时间序列预测时间序列预测
背景情况:
- 园艺商品价格波动,以香为例,影响农民,贸易商和消费者.
- 准确的价格预测对于市场稳定和印度园艺部门的利能力至关重要.
- 传统的统计模型在捕捉复杂的价格动态方面存在局限性.
研究的目的:
- 为了比较印度香价格预测的各种统计和机器学习模型的有效性.
- 确定最准确的模型来预测园艺商品价格.
- 为了解决在印度采用机器学习用于农业价格预测的不情愿.
主要方法:
- 配备了自回归集成移动平均 (ARIMA),季节性ARIMA (SARIMA),ARCH,GARCH,人工神经网络 (ANN) 和循环神经网络 (RNN) 模型.
- 利用了印度古吉拉特邦2009年1月至2019年12月的香价格数据.
- 使用平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE),SMAPE,MASE和平均定向精度 (MDA) 评估模型性能.
主要成果:
- 与所有其他模型相比,递归神经网络 (RNN) 显示出更高的预测准确性.
- 机器学习方法,特别是RNN,超越了传统的随机模型.
- 在所有评估的准确度指标中,RNN实现了最低的错误指标.
结论:
- 在印度背景下,RNN是准确预测香价格的最有效模型.
- 像ARIMA,SARIMA,ARCH,GARCH和ANN这样的统计模型的准确性较低.
- 这项研究证实了先进的机器学习技术在农业价格预测方面的优势.
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