用模糊逻辑增强Transformer，提升时间序列预测的准确性与可解释性。
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated Transformers
- 将模糊隶属函数融入自注意力机制，动态处理数据不确定性。
- 在真实数据集上，预测与异常检测性能显著优于传统Transformer。
- 适合处理含噪、模糊的时间序列，尤其适用于工业监控等场景。
本文提出FANTF（基于模糊注意力网络的Transformer），通过将模糊逻辑整合到现有Transformer架构中，提升时间序列预测、分类和异常检测性能。FANTF引入一种新的模糊注意力机制，利用模糊隶属函数处理噪声和模糊数据中的不确定性。该方法通过在Transformer的自注意力模块中嵌入模糊逻辑原理，增强对复杂时序依赖性和多变量关系的捕捉能力。框架结合模糊增强注意力与多个基准Transformer模型，实现高效预测、分类与异常检测。具体而言，FANTF生成可学习的模糊注意力分数，突出不同时序特征与数据点的重要性，提供决策过程的可解释性。在多个真实世界数据集上的实验表明，相较于传统Transformer模型，FANTF在预测、分类与异常检测任务中均有显著性能提升。
原文摘要 · Abstract (English)
This paper introduces FANTF (Fuzzy Attention Network-Based Transformers), a novel approach that integrates fuzzy logic with existing transformer architectures to advance time series forecasting, classification, and anomaly detection tasks. FANTF leverages a proposed fuzzy attention mechanism incorporating fuzzy membership functions to handle uncertainty and imprecision in noisy and ambiguous time series data. The FANTF approach enhances its ability to capture complex temporal dependencies and multivariate relationships by embedding fuzzy logic principles into the self-attention module of the existing transformer's architecture. The framework combines fuzzy-enhanced attention with a set of benchmark existing transformer-based architectures to provide efficient predictions, classification and anomaly detection. Specifically, FANTF generates learnable fuzziness attention scores that highlight the relative importance of temporal features and data points, offering insights into its decision-making process. Experimental evaluatios on some real-world datasets reveal that FANTF significantly enhances the performance of forecasting, classification, and anomaly detection tasks over traditional transformer-based models.
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