Open AccessAUNET (Attention-based Unified Network): Leveraging Attention Based N-BEATS for Enhanced Univariate Time Series Forecasting
Author(s) -
Adria Binte Habib,
Md. Golam Rabiul Alam,
Md. Zia Uddin
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3574459
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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 2 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 2 of 0.9990, and in healthcare, it outperforms baselines in RMSE and R 2 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.