Scaling Up Multivariate Time Series Pre-Training with Decoupled Spatial-Temporal Representations

Data scale has been acknowledged as a crucial factor for enhancing the generalization and effectiveness of pre-training models. While existing methods of multivariate time series pre-training are primarily limited to a single specific dataset, scaling to a larger scenario that includes multiple diverse datasets (e.g., multi-region data) remains a substantial challenge. In this paper, we present a novel Decoupled Spatial-Temporal Representation Learning (DeSTR) framework to serve as the backbone network for investigating the data scaling capability of multivariate time series pre-training architectures. Specifically, DeSTR utilizes two separate encoders to capture both the temporal dynamics within each time series and the spatial correlations among multiple variables. The obtained representations of distinct modalities are then fed into a Spatial-Guided Temporal Transformer to equip the temporal features with spatial discriminative information. Moreover, we employ masked autoencoding as the foundational pre-training framework and introduce spacetime-agnostic augmentation to improve robustness and facilitate implicit spatiotemporal modeling. Finally, we successfully pre-train a unified time series representation learning framework on real-world datasets from three different cities. Extensive experiments are carried out on various downstream tasks to validate the performance of DeSTR, compared with three categories of state-of-the-art baselines: deep sequential models, spatial-temporal graph neural networks, and time series representation learning methods. The results clearly demonstrate the advantages of scaling multivariate time series pre-training to multiple datasets, highlighting the effectiveness of DeSTR as a general spatiotemporal learner.