  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
