  Human activities generate various event sequences such as taxi trip records,
bike-sharing pick-ups, crime occurrence, and infectious disease transmission.
The point process is widely used in many applications to predict such events
related to human activities. However, point processes present two problems in
predicting events related to human activities. First, recent high-performance
point process models require the input of sufficient numbers of events
collected over a long period (i.e., long sequences) for training, which are
often unavailable in realistic situations. Second, the long-term predictions
required in real-world applications are difficult. To tackle these problems, we
propose a novel meta-learning approach for periodicity-aware prediction of
future events given short sequences. The proposed method first embeds short
sequences into hidden representations (i.e., task representations) via
recurrent neural networks for creating predictions from short sequences. It
then models the intensity of the point process by monotonic neural networks
(MNNs), with the input being the task representations. We transfer the prior
knowledge learned from related tasks and can improve event prediction given
short sequences of target tasks. We design the MNNs to explicitly take temporal
periodic patterns into account, contributing to improved long-term prediction
performance. Experiments on multiple real-world datasets demonstrate that the
proposed method has higher prediction performance than existing alternatives.
