The widespread deployment of sensing devices leads to a surge in data for spatio-temporal forecasting applications such as traffic flow, air quality, and wind energy. Although spatio-temporal graph neural networks have achieved success in modeling various static spatio-temporal forecasting scenarios, real-world spatio-temporal data are typically received in a streaming manner, and the network continuously expands with the installation of new sensors. Thus, spatio-temporal forecasting in streaming scenarios faces dual challenges: the inefficiency of retraining models over newly arrived data and the detrimental effects of catastrophic forgetting over long-term history. To address these challenges, we propose a novel prompt tuning-based continuous forecasting method, following two fundamental tuning principles guided by empirical and theoretical analysis: expand and compress, which effectively resolve the aforementioned problems with lightweight tuning parameters. Specifically, we integrate the base spatio-temporal graph neural network with a continuous prompt pool, utilizing stored prompts (i.e., few learnable parameters) in memory, and jointly optimize them with the base spatio-temporal graph neural network. This method ensures that the model sequentially learns from the spatio-temporal data stream to accomplish tasks for corresponding periods. Extensive experimental results on multiple real-world datasets demonstrate the multi-faceted superiority of our method over the state-of-the-art baselines, including effectiveness, efficiency, universality, etc.

翻译：传感设备的广泛部署导致交通流量、空气质量及风能等时空预测应用的数据激增。尽管时空图神经网络在建模各类静态时空预测场景中已取得成功，但现实世界的时空数据通常以流式方式接收，且网络会随着新传感器的安装而持续扩展。因此，流式场景下的时空预测面临双重挑战：基于新到数据重新训练模型的低效性，以及对长期历史数据的灾难性遗忘带来的负面影响。为应对这些挑战，我们提出一种基于提示调优的新型连续预测方法，该方法遵循经验与理论分析指导的两项基本调优原则——扩展与压缩，从而以轻量级调优参数有效解决上述问题。具体而言，我们将基础时空图神经网络与连续提示池相结合，利用内存中存储的提示（即少量可学习参数），并将其与基础时空图神经网络联合优化。该方法确保模型能够从时空数据流中顺序学习，以完成对应时段的任务。在多个真实世界数据集上的大量实验结果证明了本方法相较于现有先进基线在多方面的优越性，包括有效性、效率、普适性等。