棉花价格指数的长期和短期记忆模型 波动风险 基于可解释的人工智能
Huosong Xia1,2,3, Xiaoyu Hou1, Justin Zuopeng Zhang4
1School of Management, Wuhan Textile University, Wuhan, China.
Big data
|November 17, 2023
概括
市场的不确定性影响决策,增加风险. 这项研究使用长短期记忆 (LSTM) 模型来分析棉花价格波动,发现它准确地反映了趋势,但不是确切的价格.
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
- 时间序列分析时间序列分析
背景情况:
- 市场不确定性对决策产生重大影响,并增加了参与者的风险.
- 棉花价格波动是复杂的,受供需,气候和政策因素的影响.
研究的目的:
- 通过分析棉花价格指数波动,降低决策风险并支持决策者.
- 整合影响棉花价格的多个因素,并开发一个预测模型.
主要方法:
- 整合了13个影响棉花价格指数波动的因素,分类为交易和相互作用数据.
- 构建和实施用于波动性分析的长短期记忆 (LSTM) 模型.
- 解释性人工智能 (XAI) 技术的应用,用于输入特征的统计分析.
主要成果:
- 该LSTM模型准确分析棉花价格指数波动趋势,但不能预测确切的价格.
- 结合交易和相互作用数据对分析棉花价格趋势比单独的交易数据更为敏感和有益.
- 可解释的人工智能增强了决策者对模型分析的信心.
结论:
- 该研究准确反映了棉花市场的波动,有助于国家,企业和农民的决策.
- 该模型有助于减轻与棉花价格波动相关的风险.
- XAI集成建立了信任,并促进了在市场分析中采用数据驱动的洞察力.
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