  Spatial-temporal forecasting systems play a crucial role in addressing
numerous real-world challenges. In this paper, we investigate the potential of
addressing spatial-temporal forecasting problems using general time series
forecasting models, i.e., models that do not leverage the spatial relationships
among the nodes. We propose a all-Multi-Layer Perceptron (all-MLP) time series
forecasting architecture called RPMixer. The all-MLP architecture was chosen
due to its recent success in time series forecasting benchmarks. Furthermore,
our method capitalizes on the ensemble-like behavior of deep neural networks,
where each individual block within the network behaves like a base learner in
an ensemble model, particularly when identity mapping residual connections are
incorporated. By integrating random projection layers into our model, we
increase the diversity among the blocks' outputs, thereby improving the overall
performance of the network. Extensive experiments conducted on the largest
spatial-temporal forecasting benchmark datasets demonstrate that the proposed
method outperforms alternative methods, including both spatial-temporal graph
models and general forecasting models.
