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.

翻译：时空预测系统在应对众多现实世界挑战中发挥着至关重要的作用。本文探讨了使用通用时间序列预测模型（即不利用节点间空间关系的模型）解决时空预测问题的潜力。我们提出了一种名为RPMixer的全多层感知机（all-MLP）时间序列预测架构。选择全MLP架构是因为其在近期时间序列预测基准测试中取得的成功。此外，我们的方法利用了深度神经网络的类集成行为特性，即当网络中加入恒等映射残差连接时，网络中的每个独立块表现得如同集成模型中的一个基学习器。通过将随机投影层集成到我们的模型中，我们增加了各块输出之间的多样性，从而提升了网络的整体性能。在最大规模的时空预测基准数据集上进行的大量实验表明，所提出的方法优于其他替代方法，包括时空图模型和通用预测模型。