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Conference Paper
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
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- Authors
- Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, Jaegul Choo
- Issue Date
- 2022-04
- Citation
- International Conference on Learning Representations (ICLR) 2022, pp.1-25
- Language
- English
- Type
- Conference Paper
- Abstract
- Statistical properties such as mean and variance often change over time in time series, i.e., time-series data suffer from a distribution shift problem. This change in temporal distribution is one of the main challenges that prevent accurate timeseries forecasting. To address this issue, we propose a simple yet effective normalization method called reversible instance normalization (RevIN), a generally-applicable normalization-and-denormalization method with learnable affine transformation. The proposed method is symmetrically structured to remove and restore the statistical information of a time-series instance, leading to significant performance improvements in time-series forecasting, as shown in Fig. 1. We demonstrate the effectiveness of RevIN via extensive quantitative and qualitative analyses on various real-world datasets, addressing the distribution shift problem.
- KSP Keywords
- Affine Transformation, Normalization method, Over time, Real-world, Statistical information, Statistical properties, Time series data, Time-series forecasting, temporal distribution