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Found 2 papers, 1 papers with code
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
In GinAR, it consists of two key components, that is, interpolation attention and adaptive graph convolution to take place of the fully connected layer of simple recursive units, and thus are capable of recovering all missing variables and reconstructing the correct spatial-temporal dependencies for recursively modeling of multivariate time series data, respectively.
AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction
However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors.
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