WinNet:time series forecasting with a window-enhanced period extracting and interacting

Recently, Transformer-based methods have significantly improved state-of-the-art time series forecasting results, but they suffer from high computational costs and the inability to capture the long and short periodicity of time series. We present a highly accurate and simply structured CNN-based model for long-term time series forecasting tasks, called WinNet, including (i) Inter-Intra Period Encoder (I2PE) to transform 1D sequence into 2D tensor with long and short periodicity according to the predefined periodic window, (ii) Two-Dimensional Period Decomposition (TDPD) to model period-trend and oscillation terms, and (iii) Decomposition Correlation Block (DCB) to leverage the correlations of the period-trend and oscillation terms to support the prediction tasks by CNNs. Results on nine benchmark datasets show that the WinNet can achieve SOTA performance and lower computational complexity over CNN-, MLP-, Transformer-based approaches. The WinNet provides potential for the CNN-based methods in the time series forecasting tasks, with perfect tradeoff between performance and efficiency.