Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting
Wei Chen Yuxuan Liang
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 (STGNNs) 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, **_EAC_**, following two fundamental tuning principles guided by empirical and theoretical analysis: _**e**xpand **a**nd **c**ompress_, which effectively resolve the aforementioned problems with lightweight tuning parameters. Specifically, we integrate the base STGNN with a continuous prompt pool, utilizing stored prompts (\ie, few learnable parameters) in memory, and jointly optimize them with the base STGNN. 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 **_EAC_** over the state-of-the-art baselines, including effectiveness, efficiency, universality, etc.
Spatio-temporal Graph, Continual Forecasting, Tuning Principle
We introduce EAC, which follows the two fundamental tuning principle and learns prompt parameters  pool only through expand and compress, simply, effectively and efficiently solving the continual spatio-temporal graph forecasting problem.