On the Role of Reversible Instance Normalization

arXiv cs.LG / 3/13/2026

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Key Points

The paper identifies three central challenges for normalization in time series forecasting: temporal input distribution shift, spatial input distribution shift, and conditional output distribution shift.
It revisits Reversible Instance Normalization (RevIN) and shows via ablation studies that several of its components are redundant or detrimental.
Based on these findings, the authors propose new perspectives to improve RevIN's robustness and generalization.
The work advances understanding of normalization in time series forecasting and may influence future model design and evaluation.

Abstract

Data normalization is a crucial component of deep learning models, yet its role in time series forecasting remains insufficiently understood. In this paper, we identify three central challenges for normalization in time series forecasting: temporal input distribution shift, spatial input distribution shift, and conditional output distribution shift. In this context, we revisit the widely used Reversible Instance Normalization (RevIN), by showing through ablation studies that several of its components are redundant or even detrimental. Based on these observations, we draw new perspectives to improve RevIN's robustness and generalization.