用图神经网络分析经济数据，提前预测美元日元汇率变化
Macroeconomic Message Passing for Anticipating Foreign Exchange Regime Changes: A Deep Logical Learning Approach using Graph Tsetlin Machines
- 将经济指标与技术指标建模为有向图，通过消息传递更新节点特征
- 在USD/JPY汇率上实现对市场格局转换的有效预测
- 模型可解释性强，适合关注金融预测与可解释AI的研究者
本文提出一种基于图论的外汇市场格局预测方法。该模型将多变量宏观经济因素与技术指标表示为超向量化的有向多重图，通过图Tsetlin机（GraphTM）框架中的消息传递机制，动态更新局部节点特征。利用深度逻辑学习能力，模型能够识别复杂的子图模式，从而有效预测美元兑日元（USD/JPY）汇率的市场格局转变。实验验证了该方法在捕捉复杂非线性关系方面的有效性，提升了对宏观驱动下市场状态切换的预判能力。
原文摘要 · Abstract (English)
This paper introduces a graph-theoretic approach for predicting market regimes in foreign exchange (FX) currency prices. Specifically, the proposed model incorporates exogenous macroeconomic variables to update localized node features via message-passing operations. Utilizing the Graph Tsetlin Machine (GraphTM) framework, we empirically demonstrate the efficacy of this approach in anticipating market regimes for the US Dollar and Japanese Yen currency pair (USD/JPY). By representing multivariate macroeconomic drivers and technical indicators as hypervectorized directed multigraphs, the GraphTM leverages structured message passing to construct deep, interpretable logical clauses capable of recognizing complex sub-graph patterns.
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