English(EN) NoRIN: Backbone-Adaptive Reversible Normalization for Time-Series Forecasting

NoRIN 通过非线性归一化推进时间序列预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 16:42

研究人员推出了一种新颖的非线性可逆归一化技术 NoRIN，用于时间序列预测，该技术超越了 RevIN 等现有方法的线性仿射变换。与 RevIN 的局限性不同，NoRIN 使用 Johnson $S_U$ 变换，其参数可以调整分布尾部和偏度。该方法将形状参数优化与梯度训练分离，使用分位数拟合和贝叶斯优化来防止模型默认为线性形式，证明了不同的网络架构受益于不同的归一化参数。 AI

影响引入了一种更灵活的归一化技术，可以提高各种时间序列预测模型的性能。

排序理由该集群包含一篇详细介绍时间序列预测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Yuyang Xiao · 2026-05-11 16:42

NoRIN：面向时间序列预测的骨干自适应可逆归一化

Reversible instance normalization (RevIN) and its successors (Dish-TS, SAN, FAN) have become the de facto plug-in for time-series forecasting, yet the map they apply to each data point is strictly affine, $x \mapsto ax+b$, so they cannot reshape the underlying distribution -- hea…

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NoRIN：面向时间序列预测的骨干自适应可逆归一化

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