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基于时间序列条件生成对抗网络的绿色金融增长预测模型
Aya Salama Abdelhady1,2, Nadia Dahmani3,4, Lobna M AbouEl-Magd2,5
1Faculty of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Canada.
PloS one
|July 24, 2024
概括
这项研究使用先进的人工智能增强了绿色金融增长预测. 条件生成对抗网络 (CT-GANs) 增强数据,改进非线性自回归神经网络 (NARNNs) 以实现更准确的投资预测.
科学领域:
- 环境金融 环境金融
- 计算金融是指计算金融.
- 时间序列分析时间序列分析
背景情况:
- 缓解气候变化需要对绿色部门进行大量投资.
- 准确预测绿色金融增长对于鼓励投资至关重要.
- 由于数据限制,现有的预测模型可能缺乏准确性.
研究的目的:
- 提出和验证一种用于预测绿色金融增长的新方法.
- 通过数据增强技术来提高预测准确度.
- 在全球范围内鼓励增加对绿色部门的投资.
主要方法:
- 利用时间序列条件生成对抗网络 (CT-GANs) 进行数据增强.
- 使用非线性自回归神经网络 (NARNNs) 进行绿色金融增长预测.
- 将该方法应用于五大洲四十个国家的数据集,并通过增强的Dickey-Fuller (ADF) 测试验证数据的非静止性.
主要成果:
- 用CT-GAN增强数据训练的NARN获得了卓越的性能 (R平方:98.8%欧洲,96.6%亚洲,99%其他地区).
- 与基线NARNN模型相比,CT-GAN增强显著改善了R平方和RMSE.
- 拟议的模型在所有地区都超过了非线性自回归外源神经网络 (NARX-NN).
结论:
- 结合CT-GAN和NARNN的拟议方法有效预测绿色金融增长.
- 用CT-GAN增强数据是改进金融时间序列预测的一个有价值的技术.
- 通过提供更可靠的增长预测,这些发现支持增加对绿色金融的投资.
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