  Spatio-Temporal prediction plays a critical role in smart city construction.
Jointly modeling multiple spatio-temporal tasks can further promote an
intelligent city life by integrating their inseparable relationship. However,
existing studies fail to address this joint learning problem well, which
generally solve tasks individually or a fixed task combination. The challenges
lie in the tangled relation between different properties, the demand for
supporting flexible combinations of tasks and the complex spatio-temporal
dependency. To cope with the problems above, we propose an Automated
Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple
spatio-temporal tasks jointly. Firstly, we propose a scalable architecture
consisting of advanced spatio-temporal operations to exploit the complicated
dependency. Shared modules and feature fusion mechanism are incorporated to
further capture the intrinsic relationship between tasks. Furthermore, our
model automatically allocates the operations and fusion weight. Extensive
experiments on benchmark datasets verified that our model achieves
state-of-the-art performance. As we can know, AutoSTL is the first automated
spatio-temporal multi-task learning method.
