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.

通过结合 Unet 和 Mixer 的方法，U-Mixer 框架能够有效地捕捉不同补丁和通道之间的局部时间依赖关系，以避免通道之间分布变化的影响，并合并低级别和高级别特征以获取全面的数据表示，从而解决时间序列预测中的非平稳性挑战。该研究通过在多个实际时间序列数据集上进行广泛实验，证明了 U-Mixer 方法的有效性和鲁棒性，并在性能上超过了最先进的方法 14.5% 和 7.7%。