MWDN: A long time series forecasting framework based on multi-scale wavelet decomposition network

Long-term time series forecasting remains a significant challenge due to complex temporal dependencies, scale variability, and noise interference. Existing deep learning methods often struggle to capture fine-grained temporal features, particularly in multivariate scenarios where spatio-temporal correlations vary across different resolutions.To address these limitations, we propose MWDN multi-scale wavelet decomposition network , a novel forecasting framework that integrates multi-scale decomposition with frequency-aware modeling. MWDN employs a wavelet-based module to iteratively decompose the input into detail and approximation sequences, effectively separating seasonal and trend components. These are then processed in parallel via a dual-branch architecture, enabling efficient modeling of variable dependencies across frequencies.To further enhance representation, a multi-scale fusion module aggregates information across resolutions, improving prediction accuracy while mitigating information loss. Extensive experiments on multiple benchmark datasets show that MWDN consistently achieves state-of-the-art or second-best performance on both short- and long-term forecasting tasks. Ablation studies validate the effectiveness of the decomposition strategy and architectural design.MWDN offers a robust and scalable solution for multivariate time series forecasting. The source code is publicly available at: https: github.com take-off-ddl MWDN.