All-Multi-Layer Perceptron (all-MLP) mixer models have been shown to be
effective for time series forecasting problems. However, when such a model is
applied to high-dimensional time series (e.g., the time series in a
spatial-temporal dataset), its performance is likely to degrade due to
overfitting issues. In this paper, we propose an all-MLP time series
forecasting architecture, referred to as RPMixer. Our method leverages the
ensemble-like behavior of deep neural networks, where each individual block
within the network acts like a base learner in an ensemble model, especially
when identity mapping residual connections are incorporated. By integrating
random projection layers into our model, we increase the diversity among the
blocks' outputs, thereby enhancing the overall performance of RPMixer.
Extensive experiments conducted on large-scale spatial-temporal forecasting
benchmark datasets demonstrate that our proposed method outperforms alternative
methods, including both spatial-temporal graph models and general forecasting
models.

我们提出了一种名为 RPMixer 的全多层感知机时间序列预测架构，通过将随机投影层集成到模型中，增强了其整体性能，并在大规模时空预测基准数据集上展开的广泛实验表明，我们的方法优于其他方法，包括时空图模型和一般预测模型。