让预测模型在测试时自适应变化的数据，提升长期预测可靠性
Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation
- 用部分真实值和门控校准模块，在测试时动态调整模型
- 在多个数据集上显著提升长期预测准确率，尤其应对分布漂移
- 适用于各类预训练模型，适合工业级时间序列部署场景
深度神经网络在时间序列预测（TSF）中取得显著进展，但时间序列的非平稳性会降低预训练模型在关键场景下的可靠性。本文提出一种面向时间序列预测的测试时自适应框架（TSF-TTA），其核心方法TAFAS能灵活适应测试分布的持续变化，同时保留预训练阶段学到的核心语义信息。通过利用部分可观测的真实标签与门控校准模块，实现对源模型的主动、鲁棒且模型无关的自适应。在多种基准数据集和前沿架构上的实验表明，TAFAS在长期预测场景中表现出色，尤其在显著分布偏移的情况下仍具优势。代码已开源：https://github.com/kimanki/TAFAS。
原文摘要 · Abstract (English)
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.
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