用神经微分方程实现时间序列预测的测试时自适应
AdaNODEs: Test Time Adaptation for Time Series Forecasting Using Neural ODEs
- 基于神经微分方程设计时间序列专用自适应框架
- 在高维数据上相对最优基线提升28.4%
- 仅更新少量参数，适合资源受限场景
测试时适应（TTA）作为一种新方法，可通过未标注的目标域数据将预训练模型适配到新的、未见过的数据分布。然而，现有方法多针对独立数据设计，很少考虑时间序列数据，更少用于预测任务。本文提出AdaNODEs，一种专为时间序列预测设计的无源TTA方法。通过利用神经常微分方程（NODEs），构建适应时间序列分布偏移特性的新框架，并创新性地提出适用于预测任务的新损失函数。该方法仅需更新少量模型参数，在捕捉时序依赖的同时避免显著内存开销。在一维和高维数据上的大量实验表明，相比当前最优基线，AdaNODEs分别取得5.88%和28.4%的相对提升，尤其在严重分布偏移下仍保持鲁棒性。
原文摘要 · Abstract (English)
Test time adaptation (TTA) has emerged as a promising solution to adapt pre-trained models to new, unseen data distributions using unlabeled target domain data. However, most TTA methods are designed for independent data, often overlooking the time series data and rarely addressing forecasting tasks. This paper presents AdaNODEs, an innovative source-free TTA method tailored explicitly for time series forecasting. By leveraging Neural Ordinary Differential Equations (NODEs), we propose a novel adaptation framework that accommodates the unique characteristics of distribution shifts in time series data. Moreover, we innovatively propose a new loss function to tackle TTA for forecasting tasks. AdaNODEs only requires updating limited model parameters, showing effectiveness in capturing temporal dependencies while avoiding significant memory usage. Extensive experiments with one- and high-dimensional data demonstrate that AdaNODEs offer relative improvements of 5.88\% and 28.4\% over the SOTA baselines, especially demonstrating robustness across higher severity distribution shifts.
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