Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In this paper, we propose a time-step-level representation learning framework for individual instances via bootstrapped spatiotemporal representation prediction. We evaluated the effectiveness and flexibility of our representation learning framework on correlated time series forecasting and cold-start transferring the forecasting model to new instances with limited data. A linear regression model trained on top of the learned representations demonstrates our model performs best in most cases. Especially compared to representation learning models, we reduce the RMSE, MAE, and MAPE by 37%, 49%, and 48% on the PeMS-BAY dataset, respectively. Furthermore, in real-world metro passenger flow data, our framework demonstrates the ability to transfer to infer future information of new cold-start instances, with gains of 15%, 19%, and 18%. The source code will be released under the GitHub https://github.com/bonaldli/Spatiotemporal-TS-Representation-Learning

翻译：相关时间序列分析在众多现实工业领域中扮演着重要角色。如何从大规模数据中学习高效表示以服务后续下游任务，既是必要任务也充满挑战。本文提出一种基于自引导时空表示预测的逐时间步表示学习框架，适用于个体实例。我们在相关时间序列预测任务及冷启动场景下（将预测模型迁移至仅含有限数据的新实例）评估了该框架的有效性与灵活性。在所学表示之上训练的线性回归模型表明，本方法在多数情况下表现最优。特别地，相较于其他表示学习模型，我们在PeMS-BAY数据集上分别将RMSE、MAE和MAPE降低了37%、49%和48%。此外，在真实地铁客流数据中，本框架展现了将模型迁移到新冷启动实例以推断未来信息的能力，三项指标分别提升15%、19%和18%。源代码将在GitHub上发布：https://github.com/bonaldli/Spatiotemporal-TS-Representation-Learning