反事实性的基于解释的加密货币价格预测
Xinxin Luo1, Wei Yin1,2
1School of Cyber Science and Engineering, Southeast University, No.2, Southeast University Road, Jiangning District, Nanjing 211189, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了CryptoForecastCF,这是一个用于可解释加密货币预测的新型模型. 它为市场动态提供了可操作的见解,以更好地管理波动的加密货币市场中的风险.
科学领域:
- 人工智能的人工智能
- 金融技术 金融技术
- 计算金融是指计算金融.
背景情况:
- 深度学习模型在加密货币预测方面表现出色,但缺乏可解释性和可信度.
- 加密货币市场表现出高波动性和复杂的非线性动态,需要强有力的风险管理.
- 了解模型对历史数据变化的敏感性对于可靠的财务预测至关重要.
研究的目的:
- 提出加密货币反事实解释 (CryptoForecastCF) 模型,以提高加密货币预测中的解释性.
- 解决用于财务预测的深度学习模型中的可信度差距.
- 为波动的加密货币市场的交易者和风险经理提供可操作的见解.
主要方法:
- 开发了CryptoForecastCF,这是一个利用渐变式优化的模型,用于反事实解释.
- 对历史市场特征 (例如价格) 确定了最小的,受规范约束的扰动 (l1或l2).
- 生成反事实解释,以引导模型预测进入用户指定的目标间隔.
主要成果:
- CryptoForecastCF阐明了不透明预测模型的关键驱动因素和决策边界.
- 该模型确定了实现预期预测结果所需的特定市场转变.
- 提供了可操作的见解,以导航高风险的场景,并减轻不利的预测.
结论:
- CryptoForecastCF提高了用于加密货币预测的深度学习模型的可解释性和可靠性.
- 该方法通过揭示模型敏感性来促进改进的风险管理.
- 可操作的反事实解释使金融专业人员能够在加密货币交易中做出更明智的决策.
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