The prediction quality of simple linear models for time series forecasting often surpasses that of deep models such as Transformers. However， on datasets with a large number of channels， deep models， particularly Multi-Layer Perceptron （MLP）， can outperform simple linear models. Aiming at the differences in error power spectrum between simple linear models and MLPs in time series forecasting， an High-frequency enhanced time series prediction model based on multi-layer perceptron — HiFNet （High-Frequency Network） was proposed. Firstly， the fitting capability of MLPs within low-frequency bands was utilized. Then， the Adaptive Series Decomposition （ASD） module and the grouped linear layer were adopted to address the overfitting issue of MLPs in high-frequency bands and the issue of channel independence strategy failing to handle the channel redundancy effectively， thereby enhancing the robustness of MLPs in high-frequency band. Finally， experiments were conducted to HiFNet on standard datasets in the fields of meteorology， power， and transportation. The results demonstrate that the Mean Squared Error （MSE） of HiFNet is reduced by up to 23.6%， 10.0%， 35.1%， and 6.5%， respectively， compared to those of NLinear， RLinear， SegRNN （Segment Recurrent Neural Network）， and PatchTST （Patch Time Series Transformer）. At the same time， the grouped linear layer alleviates the impact of the channel redundancy by learning low-rank representations related to channels.