Abstract
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.
Abstract (translated)
测试时间自适应（TTA）作为一种有前景的解决方案，能够利用未标记的目标域数据来调整预训练模型以应对新的、未曾见过的数据分布。然而，大多数TTA方法是为独立数据设计的，往往忽略了时间序列数据，并且很少解决预测任务。本文介绍了AdaNODEs，一种专为时间序列预测量身定制的源无关TTA方法。通过利用神经常微分方程（NODEs），我们提出了一种新颖的适应框架，该框架能够容纳时间序列数据中分布变化的独特特征。此外，我们创新性地提出了一个新的损失函数来解决针对预测任务的TTA问题。AdaNODEs只需更新少量模型参数，在捕获时间依赖关系的同时避免了显著的记忆消耗增加。 广泛的实验结果表明，无论是单维还是高维度的数据上，AdaNODEs相较于最先进的基线方法分别提供了5.88%和28.4%的相对改进，尤其是在面对更高严重程度分布变化时表现出更强的鲁棒性。
URL
https://arxiv.org/abs/2601.12893
