Online Algorithm for Node Feature Forecasting in Temporal Graphs
Aniq Ur Rahman Justin P. Coon
In this paper, we propose an online algorithm "mspace" for forecasting node
features in temporal graphs, which adeptly captures spatial cross-correlation
among different nodes as well as the temporal autocorrelation within a node.
The algorithm can be used for both probabilistic and deterministic multi-step
forecasting, making it applicable for estimation and generation tasks.
Comparative evaluations against various baselines, including graph neural
network (GNN) based models and classical Kalman filters, demonstrate that
mspace performs at par with the state-of-the-art and even surpasses them on
some datasets. Importantly, mspace demonstrates consistent robustness across
datasets with varying training sizes, a notable advantage over GNN-based
methods requiring abundant training samples to learn the spatiotemporal trends
in the data effectively. Therefore, employing mspace is advantageous in
scenarios where the training sample availability is limited. Additionally, we
establish theoretical bounds on multi-step forecasting error of mspace and show
that it scales as $O(q)$ for $q$-step forecast.