U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for
  Time Series Forecasting
Xiang Ma Xuemei Li Lexin Fang Tianlong Zhao Caiming Zhang
Time series forecasting is a crucial task in various domains. Caused by
factors such as trends, seasonality, or irregular fluctuations, time series
often exhibits non-stationary. It obstructs stable feature propagation through
deep layers, disrupts feature distributions, and complicates learning data
distribution changes. As a result, many existing models struggle to capture the
underlying patterns, leading to degraded forecasting performance. In this
study, we tackle the challenge of non-stationarity in time series forecasting
with our proposed framework called U-Mixer. By combining Unet and Mixer,
U-Mixer effectively captures local temporal dependencies between different
patches and channels separately to avoid the influence of distribution
variations among channels, and merge low- and high-levels features to obtain
comprehensive data representations. The key contribution is a novel
stationarity correction method, explicitly restoring data distribution by
constraining the difference in stationarity between the data before and after
model processing to restore the non-stationarity information, while ensuring
the temporal dependencies are preserved. Through extensive experiments on
various real-world time series datasets, U-Mixer demonstrates its effectiveness
and robustness, and achieves 14.5\% and 7.7\% improvements over
state-of-the-art (SOTA) methods.