A Self Attention Neuro Fuzzy Inference System for Time Series Prediction1

This paper proposes Self Attention Neuro Fuzzy Inference System (SANFIS), a novel framework for time series prediction, to address the challenges of rule explosion and accuracy improvement in deep fuzzy systems. SANFIS introduces an input universe of discourse mapping to define fuzzy sets, enabling joint optimization of antecedent and consequent parameters through gradient descent. By integrating SANFIS into a Deep Convolutional Fuzzy System (DCFS), the resulted model SANFIS-DCFS hierarchically reduces the number of rules while enhancing nonlinear approximation capabilities. Experimental results on Mackey-Glass chaotic time series and water quality prediction tasks demonstrate that SANFIS-DCFS achieves significantly lower testing RMSE values (0.0010 and 0.00497, respectively) compared to existing methods, with an 84.6% reduction in test error for chaotic series. The proposed framework not only mitigates the rule explosion problem but also improves prediction accuracy, offering a practical solution for high-dimensional time series forecasting.