  Deep learning models have recently achieved significant performance
improvements in time series forecasting. We present a highly accurate and
simply structured CNN-based model with only one convolutional layer, called
WinNet, including (i) Sub-window Division block to transform the series into 2D
tensor, (ii) Dual-Forecasting mechanism to capture the short- and long-term
variations, (iii) Two-dimensional Hybrid Decomposition (TDD) block to decompose
the 2D tensor into the trend and seasonal terms to eliminate the
non-stationarity, and (iv) Decomposition Correlation Block (DCB) to leverage
the correlation between the trend and seasonal terms by the convolution layer.
Results on eight benchmark datasets demonstrate that WinNet can achieve SOTA
performance and lower computational complexity over CNN-, MLP- and
Transformer-based methods. The code will be available at:
https://github.com/ouwen18/WinNet.
