English(EN) Online-Adaptive Fuzzy Cognitive Maps for Neuro-symbolic Financial Forecasting

神经符号框架通过自适应FCM和LSTM增强金融预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种新颖的神经符号金融预测框架，该框架将自适应模糊认知图（FCM）与长短期记忆（LSTM）网络相结合。该方法通过捕捉时间模式和因果驱动因素，并在训练后适应市场变化，解决了现有模型的局限性。该框架在纳斯达克综合指数数据上表现出改进的性能，在收益符号准确性方面优于标准LSTM模型，在状态方向准确性方面优于独立的FCM。 AI

影响这种神经符号方法可能带来更强大、更具适应性的AI驱动的金融分析工具。

排序理由该集群包含一篇详细介绍金融预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Brandon Rodriguez, Abel Deulofeu, Olaoluwa Adigun · 2026-11-11 05:00

用于神经符号金融预测的在线自适应模糊认知图

arXiv:2611.13388v1 Announce Type: new Abstract: We present a neuro-symbolic framework that pairs online-adaptive Fuzzy Cognitive Maps (FCMs) with long short-term memory (LSTM) networks for financial forecasting. Financial markets exhibit regime shifts and nonlinear dynamics. Exis…

本文如何被排名

报道来源 [1]

用于神经符号金融预测的在线自适应模糊认知图

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