DWDMixer: Detail-Aware Wavelet Decomposition and Mixing for Time Series Forecasting in IoT Systems

Accurate time series forecasting is essential for proactive management and resource allocation in intelligent IoT systems, where data are often affected by sensor noise, multi-scale periodicity, and cross-frequency interactions. Although decomposition-based frameworks are widely adopted, most existing approaches rely on time-domain modeling with limited nonlinear capacity or treat frequency decomposition as a generic preprocessing step. Such designs fail to align model inductive bias with the intrinsic multi-scale structure of seasonal dynamics, leading to incomplete seasonal representations and degraded forecasting performance. To address this limitation, we propose Detail-aware Wavelet Decomposition and Mixing (DWDMixer), a seasonal-centric forecasting architecture built on multi-scale representation learning and seasonal-trend decomposition, where trend components serve as complementary signals. To enable fine-grained seasonal modeling, we propose a Detail-aware Wavelet Transform Hybrid (DWTH) block that performs hierarchical wavelet decomposition guided by detail-sensitive representations. DWTH captures cross-scale and cross-frequency interactions while jointly modeling temporal and spectral information, yielding expressive multi-resolution representations. Extensive experiments on 10 benchmark datasets demonstrate that DWDMixer significantly outperforms state-of-the-art models, achieving error reductions of up to 9% in MSE for long-term forecasting and 22% in MAE for short-term forecasting. Our code is available at https://github.com/zpc2002zpc/DWDMixer.