关键词: 时间序列预测, 时频分析, Transformer, 通道独立, 通道混合

Abstract: 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. The model aimed to mitigate data drift, suppress noise interference, and improve prediction accuracy. First, the original time series data was processed through window overlapping slices, and the statistical distribution of data in each slice was calculated and normalized. Then, a multi-layer perceptron was used to predict the statistical distribution of future data. Next, the normalized sequence underwent adaptive time-frequency conversion, and the correlation features within the frequency domain and between channels were strengthened through the channel independent encoder and the channel interactive learner 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 statistical distribution of future data was used to perform an inverse normalization operation to generate the final prediction result. Comparative experiments with the current mainstream time series prediction model PatchTST, showed that Mean Square Error (MSE) on the Exchange, ETTm2, and Solar datasets was reduced by approximately 5.3%, and Mean Absolute Error (MAE) was reduced by approximately 4.0%, demonstrating good noise suppression capabilities and prediction performance. Ablation experiments further showed that the data statistical distribution sensing, adaptive frequency domain processing, and dual-channel fusion modules made significant contributions to improving prediction accuracy.

Key words: time series prediction, time-frequency analysis, Transformer, channel independence, channel mixing