WinNet:time series forecasting with a window-enhanced period extracting
  and interacting
Wenjie Ou Dongyue Guo Zheng Zhang Zhishuo Zhao Yi Lin
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.