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

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