Time-frequency-based pyramid channel network for long-term time series forecasting

Many time-domain and frequency-domain based methods have been proposed for long-term time series forecasting. In order to obtain the seasonal correlation of different channels and time series features at different time scales, we propose a brand-new time-frequency-based pyramid channel network (TPCNet) for long-term time series forecasting. Particularly, we first build a multi-channel seasonal feature attention residual fusion structure to obtain seasonal correlations between different channels by using the short-time Fourier transform, residual ideas, and fusion operations of multiple kernels’ different channels. We then propose a dual-dimensional attention residual pyramid structure to obtain time series features at different time scales by using tensor summation operations, residual ideas, and attention mechanisms. Finally, we obtain time-series prediction results through fully connected operations. Our proposed TPCNet shows competitive prediction performance when compared with many sample classical methods on GeForce RTX 4060Ti, according to the results of experiments on six commonly used time series datasets.