Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks
<|reference_start|>Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks: Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes in a graph. However, current methods often rely on Recurrent Neural Networks (RNNs), leading to increased runtimes and memory use. Moreover, these methods typically operate within 1-hop neighborhoods, exacerbating the reduction of the receptive field. Causal Graph Processes (CGPs) offer an alternative, using graph filters instead of MLP layers to reduce parameters and minimize memory consumption. This paper introduces the Causal Graph Process Neural Network (CGProNet), a non-linear model combining CGPs and GNNs for spatiotemporal forecasting. CGProNet employs higher-order graph filters, optimizing the model with fewer parameters, reducing memory usage, and improving runtime efficiency. We present a comprehensive theoretical and experimental stability analysis, highlighting key aspects of CGProNet. Experiments on synthetic and real data demonstrate CGProNet's superior efficiency, minimizing memory and time requirements while maintaining competitive forecasting performance.<|reference_end|>
@article{einizade2024spatiotemporal,
    title={Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural
  Networks},
    author={Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo},
    journal={arXiv preprint arXiv:2405.18879},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18879},
    primaryClass={cs.LG}
}