Enhancing Time Series Forecasting with Fuzzy Attention-Integrated Transformers

TL;DR

This work introduces FANTF, a fuzzy attention-enhanced transformer for multivariate time-series analysis, integrating learnable fuzziness into the self-attention mechanism to better handle uncertainty in noisy data. By embedding fuzzy logic into FAN within existing transformer backbones, the approach maintains computational efficiency while improving forecasting, classification, and anomaly detection performance. Extensive experiments across diverse datasets show consistent gains over state-of-the-art models and ablation studies validate the contribution of the fuzzy attention component. The results suggest that learnable fuzziness enhances robustness and interpretability, with potential extensions to larger planning and decision-support systems, including future exploration with LLM-based time-series workflows.

Abstract

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.

Figures (6)

• Figure 1: Overall approach of FANTF
• Figure 2: Architecture of FANTF internal blocks
• Figure 3: Comparison of models efficiency in terms of forecasting
• Figure 4: Prediction graphs on Exchange dataset (prediction length:96)
• Figure 5: Prediction graphs on PEMS08 dataset for prediction length 48 for three FANTF approach