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：关联时间序列分析在众多现实工业领域中扮演着重要角色。为此，学习大规模数据的高效表示以支持后续下游任务既必要又充满挑战。本文提出了一种面向单个实例的时间步级表示学习框架，该框架通过自举式时空表示预测实现。我们在关联时间序列预测任务及冷启动场景下（将预测模型迁移至数据有限的新实例）评估了该表示学习框架的有效性与灵活性。基于所学表示训练的线性回归模型表明，我们的方法在多数情况下表现最优。尤其是在PeMS-BAY数据集上，相较于其他表示学习模型，我们将均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别降低了37%、49%和48%。此外，在真实地铁客流数据中，我们的框架展现了迁移至冷启动新实例并推断其未来信息的能力，三个评估指标分别提升15%、19%和18%。源代码将在GitHub（https://github.com/bonaldli/Spatiotemporal-TS-Representation-Learning）上开源。