Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy. Urban spatio-temporal data exhibits temporal mirage: similar short-window inputs have divergent future trends, and vice versa. Existing spatio-temporal graph neural networks (STGNNs) cannot effectively identify such mirages. We argue that the core reason lies in the short-window inputs that have incomplete period observation, heterogeneous global spatial correlation, and cross-period superposition causality. To bridge this gap, we develop a novel Multi- Period Pattern Pre-training (MP3), a plug-and-play pre-training plugin for distinguishing temporal mirages. MP3 presents two core innovations: (1) The multi-period pattern learning is designed to learn multi-period patterns from long time series. Specifically, multi-period temporal modeling leverages edge convolution to identify different multi-period patterns. Multi-period spatial modeling uses a bottleneck project and a global memory bank to capture heterogeneous global spatial relations efficiently. Cross-period pattern interaction employs a causality-enhanced Transformer to capture dependencies across different period patterns. (2) This plugin can seamlessly integrate into existing STGNN backbones to strengthen their forecasting performance. The experiment on five STGNN baselines across five real-world datasets (including a large-scale dataset CA) verify the effectiveness, superior scalability and strong adaptability of MP3, which brings consistent and robust performance improvements across all evaluated baselines. On average, MP3 reduces the MAE 4.7% and the RMSE 5.0%. The code can be available at https://github.com/YAN-outlook/MP3.

翻译：时空预测在交通、气候和能源等众多领域至关重要。城市时空数据呈现出时间幻象现象：相似的短时窗输入可能对应截然不同的未来趋势，反之亦然。现有时空图神经网络无法有效识别此类幻象。我们认为其核心原因在于短时窗输入存在周期观测不完整、异质性全局空间关联以及跨周期叠加因果效应。为弥补这一不足，我们提出了一种新颖的多周期模式预训练方法——MP3，这是一种用于区分时间幻象的即插即用式预训练插件。MP3包含两项核心创新：（1）多周期模式学习模块，旨在从长时序中学习多周期模式。具体而言，多周期时间建模利用边卷积识别不同多周期模式；多周期空间建模采用瓶颈投影与全局记忆库高效捕获异质性全局空间关联；跨周期模式交互则通过因果增强型Transformer捕捉不同周期模式间的依赖关系。（2）该插件可无缝集成至现有STGNN主干网络以增强其预测性能。在五个真实世界数据集（含大规模数据集CA）上对五种STGNN基线模型的实验验证了MP3的有效性、卓越可扩展性与强适应性，其为所有评估基线带来了持续稳健的性能提升。平均而言，MP3使MAE降低4.7%，RMSE降低5.0%。代码可访问https://github.com/YAN-outlook/MP3获取。