<ul><li>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.</li><li>FANTF leverages a proposed fuzzy attention mechanism incorporating fuzzy membership functions to handle uncertainty and imprecision in noisy and ambiguous time series data.</li><li>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.</li><li>Experimental evaluations on real-world datasets show that FANTF significantly enhances the performance of forecasting, classification, and anomaly detection tasks over traditional transformer-based models.</li></ul>

Enhancing Time Series Forecasting with Fuzzy Attention-Integrated Transformers

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