U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting

• URL: http://arxiv.org/abs/2401.02236v1
• Date: Thu, 4 Jan 2024 12:41:40 GMT
• Title: U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
• Authors: Xiang Ma, Xuemei Li, Lexin Fang, Tianlong Zhao, Caiming Zhang
• Abstract summary: Non-stationarity in time series forecasting obstructs stable feature propagation through deep layers, disrupts feature distributions, and complicates learning data distribution changes. We propose U-Mixer, which captures local temporal dependencies between different patches and channels separately. We show that U-Mixer achieves 14.5% and 7.7% improvements over state-of-the-art (SOTA) methods.
• Score: 11.55346291812749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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