Time series forecasting finds widespread applications in such fields as weather forecasting, power load forecasting, and financial management. In recent years, deep learning has made remarkable progress in these tasks. However, existing models still have limitations in struggling with non-stationarity and heterogeneous pattern modeling, which is mainly represented by homogenized modeling of trends and seasonal components, and modal aliasing during decomposition. To this end, this study proposes a time-frequency cooperative decomposition network, termed as DTFNet, which designs a heterogeneous architecture with parallel time and frequency domains. In the time domain, an MLP network with strong noise resistance is employed to model the long-term evolution characteristics of trends, while in the frequency domain, fast Fourier transform is adopted to extract periodic seasonal components, with multi-scale convolution operations employed to capture spatial correlations between time-frequency characteristics. Meanwhile, this study introduces a decomposition method based on discrete wavelet transformation (DWT) to replace conventional moving average decomposition, effectively mitigating boundary effects and modal aliasing. Experiments on six public datasets demonstrate that DTFNet outperforms the current mainstream models in both accuracy and robustness. Ablation experiments show the notable effectiveness of the proposed DWT-based decomposition module and dual-domain time-frequency modeling architecture. Featuring sound generalization ability, DTFNet is applicable to multiple time series forecasting tasks, offering powerful support for real-world applications such as power load forecasting and weather forecasting.