神经符号框架：在线自适应模糊认知图结合 LSTM 做金融预测
Online-Adaptive Fuzzy Cognitive Maps for Neuro-symbolic Financial Forecasting
做量化或金融建模的可以看看这篇：用带引用的 FCM 图谱让预测过程可审计，收益符号准确率比纯 LSTM 高 7.6 个百分点，换市场状态后 10-15 天就能自适应。想解释模型为什么这么预测的团队会感兴趣。
一项研究提出神经符号框架，将在线自适应 Fuzzy Cognitive Maps（FCM）与 LSTM 网络配对用于金融预测。
Framework I 从双源价格序列与宏观经济指标构建 FCM，通过误差驱动的 Hebbian 规则在线更新边权重，并用 Asymmetric Additive Residual Fusion 方法融合 FCM 与 LSTM 的预测。
在 NASDAQ Composite 数据上，Framework I 在状态方向准确率上与 LSTM 基线持平，在收益符号准确率上高出 LSTM 基线 7.6 个百分点，并在两项指标上均超过独立 FCM 模型；
自适应图在 10-15 个交易日内收敛到新市场状态。Framework II 则构建基于文献、LLM 增强的 FCM，每条边附带可验证引用，形成可审计的因子交互图谱，其中心节点模型的状态方向准确率达 68.
9% 至 85.7%。
现有时序模型往往只捕捉时间模式而隐藏因果驱动，因果图模型又难以在训练后继续适应，该工作试图同时兼顾两者。
Online-Adaptive Fuzzy Cognitive Maps for Neuro-symbolic Financial Forecasting
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. Existing models often trade off one of these key properties: these models capture temporal patterns but hide causal drivers, or they recover causal graphs that cannot adapt post-training. We close this gap with two frameworks. Framework I constructs an FCM from live dual-source price series and macroeconomic indicators, and adapts its edges online through an error-driven Hebbian rule. We introduce the Asymmetric Additive Residual Fusion method to seamlessly combine the FCM and LSTM predictions under Framework I. Framework II constructs a literature-grounded, LLM-augmented FCM where every edge carries a verifiable citation that gives an auditable map of factor interactions. On NASDAQ Composite data, Framework I matched the performance of the LSTM baseline on state-directional accuracy and outperformed LSTM by 7.6 percentage points on the return-sign accuracy. Framework I outperformed standalone FCM models across both evaluation metrics. The adaptive maps converged to new regimes within 10-15 trading days, and Framework II center-node FCMs scored 68.9% to 85.7% on the state-directional accuracy.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Fuzzy Cognitive Maps、LSTM、金融预测、神经符号，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Brandon Rodriguez, Abel Deulofeu, Olaoluwa Adigun），经 AITOP 多维评分管线结构化提炼。