GLNet: Hierarchical Global-Local Modeling for Time Series Forecasting

Long-term time series forecasting requires models that can capture long-range dependencies while maintaining computational efficiency. Although Transformer-based approaches have achieved strong performance, their quadratic complexity and potential overfitting issues limit scalability, while lightweight linear models often struggle to capture complex temporal dynamics. To address this gap, we propose Global-Local Network (GLNet), a unified yet branch-specialized architecture that integrates global sequence modeling with local temporal refinement. The core of GLNet is the Global-Local Block (GLB), a dimension-agnostic operator that combines pointwise linear projections for global temporal mixing with convolutions for local pattern extraction. GLNet adopts a decomposition-based dual-flow design: the Seasonal Flow applies hierarchical patch-wise GLB modeling to capture local waveform variations and long-range periodic evolution, while the Trend Flow employs an activation-free global trend operator to model smooth long-term dynamics. Extensive experiments on eight real-world datasets demonstrate that GLNet achieves leading mean forecasting accuracy while maintaining faster inference speed and lower memory consumption compared with existing approaches.