带有外源变量的混合时间序列模型,用于改善印度主要拉比作物的产量预测
Pramit Pandit1, Atish Sagar2, Bikramjeet Ghose3
1Department of Agricultural Statistics & Computer Application, Rabindra Nath Tagore Agriculture College, Birsa Agricultural University, Ranchi, 834006, India.
Scientific reports
|December 14, 2023
概括
阿里马克斯-LSTM混合模型显著提高了印度拉比作物的作物产量预测准确度. 这种先进的模型,结合灌数据,优于传统方法和个人模型,提供更好的RMSE和MAPE值.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 准确的作物产量预测对于农业规划和管理至关重要.
- 传统的时间序列模型经常与复杂的农业数据模式作斗争.
研究的目的:
- 评估各种混合时间序列模型的有效性,以预测印度主要的拉比作物产量.
- 评估将"灌面积 (%) "作为外部变量所产生的影响.
主要方法:
- 混合型号的比较:阿里马克斯-TDNN,阿里马克斯-NLSVR,阿里马克斯-WNN,阿里马克斯-CNN,阿里马克斯-RNN和阿里马克斯-LSTM.
- 在ARIMAX框架内使用"灌面积 (%) "作为外部变量.
- 与个人ARIMA模型和其他混合结构进行基准测试.
主要成果:
- 阿里马克斯-LSTM混合动力车型表现出卓越的性能,超过了所有其他车型.
- 使用ARIMAX-LSTM观察到根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 的显著平均改善.
- 混合型号通常表现出比单个对应型号更好的性能.
结论:
- 通过利用灌数据和LSTM的非线性处理,ARIMAX-LSTM模型提供了增强的作物产量预测准确度.
- 该研究强调了混合建模方法对农业预测单个模型的好处.
- 结果警告不要将单个模型的性能概括为它们的混合形式.
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