Aiming at the prediction difficulties caused by periodic complexity and high-frequency noise in time series data， a time series prediction model based on statistical distribution sensing and frequency domain dual-channel fusion was proposed to mitigate data drift， suppress noise interference， and improve prediction accuracy. Firstly， the original time series data was processed through window overlapping slices， the statistical distribution of data in each slice was calculated and normalized， and a MultiLayer Perceptron （MLP） was used to predict the statistical distribution of future data. Then， adaptive time-frequency transformation was performed to the normalized series， and the correlation features within the frequency domain and between channels were strengthened through the channel independent encoder and the channel interactive learner， so as to obtain multi-scale frequency domain representation. Finally， a linear prediction layer was used to complete the inverse transformation from the frequency domain to the time domain. In the output stage， the model used the statistical distribution of future data to perform inverse normalization，thereby generating the final prediction results. Comparison experimental results of the proposed model with the current mainstream time series prediction model PatchTST （Patch Time Series Transformer） show that on the Exchange， ETTm2， and Solar datasets， the model has the Mean Square Error （MSE） reduced by average of 5.3%， and the Mean Absolute Error （MAE） reduced by average of 4.0%， demonstrating good noise suppression capabilities and prediction performance. Ablation experimental results show that the all of data statistical distribution sensing， adaptive frequency domain processing， and dual-channel fusion modules have significant contributions for improving prediction accuracy.