AUNET (Attention-Based Unified Network): Leveraging Attention-Based N-BEATS for Enhanced Univariate Time Series Forecasting

Univariate time series forecasting is pivotal in domains such as climate modeling, finance, and healthcare, where both short-term precision and long-term reliability are essential. This study introduces AUNET (Attention-based Unified Network)—a novel extension of the N-BEATS model—integrating multi-head self-attention to capture both short-term fluctuations and long-term dependencies while reducing architectural redundancy and enhancing interpretability. Empirical evaluations on the (baseline dataset for this research) CIMIS weather dataset show that AUNET achieves a 0.3580 MAE, 0.4632 RMSE, 0.68% MAPE, and an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9988, outperforming baseline models including N-BEATS and attention-augmented variants by margins exceeding 87% in MAE and RMSE. AUNET’s generalization capabilities are further validated on diverse domains: in finance it attains an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9990, and in healthcare, it outperforms baselines in RMSE and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> despite inherent volatility. Statistical assessments confirm the significance and robustness of these improvements. Moreover, AUNET demonstrates reduced training time, lower FLOPs, and higher convergence stability. Its attention-guided modular architecture not only enhances predictive accuracy but also promotes transparency, making AUNET a compelling candidate for deployment in high-stakes, real-world forecasting tasks across varied domains.