Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. Nonetheless, the non-stationarity of time series undermines the reliability of pre-trained source time series forecasters in mission-critical deployment settings. In this study, we introduce a pioneering test-time adaptation framework tailored for TSF (TSF-TTA). TAFAS, the proposed approach to TSF-TTA, flexibly adapts source forecasters to continuously shifting test distributions while preserving the core semantic information learned during pre-training. The novel utilization of partially-observed ground truth and gated calibration module enables proactive, robust, and model-agnostic adaptation of source forecasters. Experiments on diverse benchmark datasets and cutting-edge architectures demonstrate the efficacy and generality of TAFAS, especially in long-term forecasting scenarios that suffer from significant distribution shifts. The code is available at https://github.com/kimanki/TAFAS.

本研究解决了时间序列预测中预训练源预测器在非平稳环境下的可靠性问题。提出的TAFAS框架能够灵活适应不断变化的测试分布，同时保持预训练期间学习的核心语义信息。实验结果表明，该方法在长时间预测场景中表现出有效性和广泛适用性，尤其是在分布显著变化的情况下。

通过测试时间适应应对时间序列预测中的非平稳性