To address the inadequate adaptability under rapidly changing data distribution and the difficulty in balancing prediction accuracy with computational overhead of the existing time series forecasting methods， a time series forecasting model based on dynamic weighted ensemble， namely TFEM （Time-Frequency Ensembled Model）， was proposed. Firstly， in the time domain module， a Low-Rank Self-Attention （LRSA） mechanism was designed to calculate attention through projecting high-dimensional features into a low-dimensional space， which reduced complexity while maintaining long-range dependency modeling. Simultaneously， in the frequency domain module， the signal was decomposed into dominant frequency components and non-stationary residuals to model global trends and local mutations， respectively， thereby enhancing the modeling capability for complex time series. Finally， at the ensemble level， a long- and short-term harmonic balanced weighting mechanism was proposed， where long-term weights was used to capture global trends robustly through recursive update， while short-term weights was used to respond to data distribution mutations promptly via a Multi-Layer Perceptron （MLP）， and a smoothing factor was incorporated to suppress violent fluctuations in weights. Experimental results demonstrate that compared with the online ensemble model OneNet （Online Network）， TFEM reduces the Mean Squared Error （MSE） by 6.4% to 44.8% and the Mean Absolute Error （MAE） by 2.8% to 17.6% on seven benchmark datasets， while reduces the parameter number by 69.4% and inference time by 50.5% on the ETTh1 dataset. It can be seen that TFEM enhances prediction accuracy while reducing computational overhead， providing a feasible solution for time series forecasting in resource-constrained scenarios.