RPMixer：利用随机投影革新大型时空数据的时间序列预测
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
AI总结：
本文提出全 MLP 架构 RPMixer，通过随机投影层增强残差模块输出多样性，使不依赖空间关系的通用时间序列模型在大型时空预测基准上超过图模型与通用预测方法。
摘要：
时空预测系统在应对众多现实挑战方面发挥着关键作用。本文研究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 all-MLP 架构是因为其近期在时间序列预测基准上取得了成功。此外，我们的方法利用了深度神经网络的类集成行为：网络中的每个独立模块都像集成模型中的基学习器，尤其是在加入恒等映射残差连接时。通过将随机投影层集成到模型中，我们提升了各模块输出之间的多样性，从而改善了网络的整体性能。在最大规模的时空预测基准数据集上开展的大量实验表明，所提方法优于其他方法，包括时空图模型和通用预测模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
