Accurate forecasting of multivariate traffic flow time series remains
challenging due to substantial spatio-temporal heterogeneity and complex
long-range correlative patterns. To address this, we propose
Spatio-Temporal-Decoupled Masked Pre-training (STD-MAE), a novel framework that
employs masked autoencoders to learn and encode complex spatio-temporal
dependencies via pre-training. Specifically, we use two decoupled masked
autoencoders to reconstruct the traffic data along spatial and temporal axes
using a self-supervised pre-training approach. These mask reconstruction
mechanisms capture the long-range correlations in space and time separately.
The learned hidden representations are then used to augment the downstream
spatio-temporal traffic predictor. A series of quantitative and qualitative
evaluations on four widely-used traffic benchmarks (PEMS03, PEMS04, PEMS07, and
PEMS08) are conducted to verify the state-of-the-art performance, with STD-MAE
explicitly enhancing the downstream spatio-temporal models' ability to capture
long-range intricate spatial and temporal patterns. Codes are available at
this https URL

通过使用掩码自编码器进行预训练，我们提出了一种新颖的框架 STD-MAE，用于准确预测多变量交通流时间序列，该框架能够学习和编码复杂的时空依赖关系，并通过隐含表示增强下游时空交通预测模型的能力。