  The widespread deployment of wireless and mobile devices results in a
proliferation of spatio-temporal data that is used in applications, e.g.,
traffic prediction, human mobility mining, and air quality prediction, where
spatio-temporal prediction is often essential to enable safety, predictability,
or reliability. Many recent proposals that target deep learning for
spatio-temporal prediction suffer from so-called catastrophic forgetting, where
previously learned knowledge is entirely forgotten when new data arrives. Such
proposals may experience deteriorating prediction performance when applied in
settings where data streams into the system. To enable spatio-temporal
prediction on streaming data, we propose a unified replay-based continuous
learning framework. The framework includes a replay buffer of previously
learned samples that are fused with training data using a spatio-temporal mixup
mechanism in order to preserve historical knowledge effectively, thus avoiding
catastrophic forgetting. To enable holistic representation preservation, the
framework also integrates a general spatio-temporal autoencoder with a
carefully designed spatio-temporal simple siamese (STSimSiam) network that aims
to ensure prediction accuracy and avoid holistic feature loss by means of
mutual information maximization. The framework further encompasses five
spatio-temporal data augmentation methods to enhance the performance of
STSimSiam. Extensive experiments on real data offer insight into the
effectiveness of the proposed framework.
