Can I read U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting on EtoBox?

U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting by Ma, Xiang; Li, Xuemei; Fang, Lexin; Zhao, Tianlong; Zhang, Caiming is a scholarly article available to read on EtoBox.

What is U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting about?

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

Author

Ma, Xiang; Li, Xuemei; Fang, Lexin; Zhao, Tianlong; Zhang, Caiming

Published

2024

Language

EN