用模糊推理+注意力机制，让股票长期预测既准又看得懂。
A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
- 结合LSTM与注意力机制提取可解释特征
- 在标普500上表现接近传统模型但更透明
- 适合需要理解预测逻辑的金融从业者
在多变量时间序列预测的复杂环境中，实现准确性和可解释性仍是重大挑战。本文提出Fuzzy Transformer（Fuzzformer），一种融合多头自注意力与模糊推理系统的新型循环神经网络架构，用于分析多变量股市数据并进行长期时间序列预测。该方法利用LSTM网络和时序注意力将多变量数据压缩为适合模糊推理系统的可解释特征。结果表明，该架构在预测性能上可媲美传统模型（如ARIMA、LSTM），同时提供网络内部有意义的信息流动。实验基于真实世界标普500指数数据，初步结果显示其在可解释预测方面具有潜力，并揭示了当前性能权衡，表明其在理解和预测股市行为中具有实际应用前景。
原文摘要 · Abstract (English)
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into interpretable features suitable for fuzzy inference systems. The resulting architecture offers comparable forecasting performance to conventional models such as ARIMA and LSTM while providing meaningful information flow within the network. The method was examined on the real world stock market index S\&P500. Initial results show potential for interpretable forecasting and identify current performance tradeoffs, suggesting practical application in understanding and forecasting stock market behavior.
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