TY  - JOUR
AU  - Wang, Qi
AU  - Nicodemas, Kelvin Amos
PY  - 2026
TI  - Hierarchical Attention Transformer for Multivariate Time Series Forecasting
JO  - Computers, Materials & Continua
SN  - 1546-2218
SP  - 78
VL  - 87
IS  - 2
AB  - Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks, where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends. However, existing Transformer-based methods often process data at a single resolution or handle multiple scales independently, overlooking critical cross-scale interactions that influence prediction accuracy. To address this gap, we introduce the Hierarchical Attention Transformer (HAT), which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism. HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks, fuses them via temperature-controlled mechanisms, and optimizes gradient flow with residual connections for stable training. Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines, with average reductions of 8.2% in MSE and 7.5% in MAE across horizons, while achieving a  6.1× training speedup over patch-based methods. These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.
UR  - https://doi.org/10.32604/cmc.2026.074305
DO  - 10.32604/cmc.2026.074305