通过小波分解分离趋势与波动，分别自适应归一化提升非平稳时间序列预测精度
Wavelet-based Disentangled Adaptive Normalization for Non-stationary Times Series Forecasting
- 用小波变换将时间序列拆分为趋势和波动成分，分治处理非平稳性
- 对强非平稳的趋势部分做一阶差分，提取稳定特征用于归一化参数生成
- 框架不依赖具体模型，可通用提升多种主干网络的预测性能
非平稳时间序列的统计特性常随时间变化，使深度模型难以泛化。实例级归一化有助于缓解时间分布偏移，但现有方法忽视了时间序列的多成分特性——不同成分表现出不同的非平稳行为。本文提出小波分解解耦自适应归一化（WDAN），一种与模型无关的框架。利用离散小波变换将输入分解为低频趋势与高频波动成分，并对各成分采用定制化归一化策略。对于具有强非平稳性的趋势部分，通过一阶差分提取稳定特征，用于预测归一化参数。在多个基准数据集上的大量实验表明，WDAN能持续提升各类主干模型的预测精度。代码已开源：https://github.com/MonBG/WDAN。
原文摘要 · Abstract (English)
Forecasting non-stationary time series is a challenging task because their statistical properties often change over time, making it hard for deep models to generalize well. Instance-level normalization techniques can help address shifts in temporal distribution. However, most existing methods overlook the multi-component nature of time series, where different components exhibit distinct non-stationary behaviors. In this paper, we propose Wavelet-based Disentangled Adaptive Normalization (WDAN), a model-agnostic framework designed to address non-stationarity in time series forecasting. WDAN uses discrete wavelet transforms to break down the input into low-frequency trends and high-frequency fluctuations. It then applies tailored normalization strategies to each part. For trend components that exhibit strong non-stationarity, we apply first-order differencing to extract stable features used for predicting normalization parameters. Extensive experiments on multiple benchmarks demonstrate that WDAN consistently improves forecasting accuracy across various backbone model. Code is available at this repository: https://github.com/MonBG/WDAN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。