Classification of weather systems (CWS) refers to the categorization of high-dimensional multivariate meteorological data into a reasonable and manageable number of typical weather systems that share similar meteorological fields, physical characteristics and evolutionary trends. Therefore, CWS has been broadly applied in weather forecasts and statistical climatology. In ensemble forecasts, CWS is utilized to generate a set of pre-defined circulation types and simplify the ensemble forecasts, where each weather pattern of ensemble members is assigned to the closest matching type, to a sequence of circulation type probabilities (

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.

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