DualNet: A dual-path network with adaptive compensation for multivariate time series forecasting

Multivariate time series prediction remains a fundamental challenge across various domains due to its complex temporal dynamics and inherent dependencies among variables. This paper introduces DualNet, an innovative architectural paradigm based on decoupling principles, which achieves effective decomposition and complementary enhancement of prediction tasks through spatial decomposition. The architecture synergistically integrates global pattern extraction and adaptive local compensation mechanisms, establishing two independent yet complementary learning spaces through functional decoupling. Our framework introduces a dual-path learning strategy that decomposes the prediction process into complementary components: a primary trajectory estimator focused on capturing inherent temporal evolution patterns, and a dedicated compensation mechanism that performs fine-grained calibration through residual learning. This decoupled design not only reduces the learning complexity of individual modules but also enhances overall prediction performance through complementary effects. We developed a temperature-scaled adaptive weighting scheme that dynamically adjusts compensation intensity based on temporal context, enabling the model to achieve optimal balance between prediction stability and adaptive refinement. Through the dual mechanisms of spatial decoupling and complementary enhancement, the architecture achieves organic unification of global feature extraction and local fine-tuning, while incorporating novel normalization strategies and hierarchical feature transformation mechanisms to enhance the model’s representational capacity. Comprehensive experiments conducted on various benchmark datasets demonstrate that this decoupling-based dual-channel architecture significantly improves the model’s capability to capture complex temporal patterns while maintaining prediction stability. The code is available at https://github.com/ZS520L/DualNet.