通过文本挖掘探索农产品预测方法的当前趋势:统计和人工智能方法的发展
Luana Gonçalves Guindani1, Gilson Adamczuk Oliveirai1, Matheus Henrique Dal Molin Ribeiro1
1Industrial & Systems Engineering Graduate Program (PPGEPS), Federal University of Technology - Parana (UTFPR), Via Do Conhecimento, KM 01 - Fraron, Pato Branco, PR, 85503-390, Brazil.
Heliyon
|December 10, 2024
概括
本研究确定了农业企业时间序列的关键预测方法,其中机器学习混合和统计模型是最普遍的. 它通过突出农业商品预测的文献差距来帮助决策.
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
- 图书统计学 图书统计学
背景情况:
- 农业是面临供应链风险的全球经济驱动力.
- 数学模型对于农业企业管理中的预测至关重要.
- 无法控制的因素需要强大的风险管理策略.
研究的目的:
- 在农业企业预测研究中自动识别主题.
- 构建2015-2022年相关研究的文献组合.
- 分析和分类农业商品分析中的预测方法.
主要方法:
- 系统的图书统计分析与潜伏迪里克莱特分配 (LDA) 相结合.
- 基于预测模型类型的30篇文章的分类:机器学习 (ML),ML-NN,ML-Ensemble,ML-混合和统计.
- 专注于用于农产品时间分析的方法.
主要成果:
- 确定的主题是"应用于农业企业时间序列的预测方法".
- 机器学习混合 (41.95%) 和统计 (29.31%) 模型是最常用的.
- 机器学习与神经网络 (ML-NN) 紧随其后的是14.94%.
结论:
- 在农业企业的预测方法中发现了文献上的差距.
- 提供了关于预测方法的实用见解,以改善决策.
- 强调了农业时间序列分析中混合和统计方法的普遍性.
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