测试时动态校准，提升时空预测在真实场景下的准确性。
Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
- 测试时引入频域校准器，实时修正周期性偏差。
- 采用流式内存队列实现高效更新，计算开销低。
- 无需复杂训练调整，适合实际部署的动态环境。
时空预测在交通、气象、能源等领域至关重要。然而现实场景常面临信号异常、噪声和分布漂移等挑战。现有方法多通过修改网络结构或训练流程提升鲁棒性，但计算成本高，难以用于大规模应用。本文提出一种新型测试时计算范式——学习校准（Learning with Calibration），即 ST-TTC，旨在捕捉测试阶段由非平稳性引发的周期性结构偏差，并实时校正预测结果以提高精度。具体而言，我们设计了具有相位-振幅调制能力的频域校准器以缓解周期漂移，并提出基于流式记忆队列的快速更新机制，实现高效的测试时计算。该方法避免了复杂的训练阶段技术，具备高效、通用、灵活的优点。在多个真实数据集上的实验表明，该方法在有效性、普适性、灵活性和效率方面均表现优异。
原文摘要 · Abstract (English)
Spatio-temporal forecasting is crucial in many domains, such as transportation, meteorology, and energy. However, real-world scenarios frequently present challenges such as signal anomalies, noise, and distributional shifts. Existing solutions primarily enhance robustness by modifying network architectures or training procedures. Nevertheless, these approaches are computationally intensive and resource-demanding, especially for large-scale applications. In this paper, we explore a novel test-time computing paradigm, namely learning with calibration, ST-TTC, for spatio-temporal forecasting. Through learning with calibration, we aim to capture periodic structural biases arising from non-stationarity during the testing phase and perform real-time bias correction on predictions to improve accuracy. Specifically, we first introduce a spectral-domain calibrator with phase-amplitude modulation to mitigate periodic shift and then propose a flash updating mechanism with a streaming memory queue for efficient test-time computation. ST-TTC effectively bypasses complex training-stage techniques, offering an efficient and generalizable paradigm. Extensive experiments on real-world datasets demonstrate the effectiveness, universality, flexibility and efficiency of our proposed method.
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