  Sparsity is a common issue in many trajectory datasets, including human
mobility data. This issue frequently brings more difficulty to relevant
learning tasks, such as trajectory imputation and prediction. Nowadays, little
existing work simultaneously deals with imputation and prediction on human
trajectories. This work plans to explore whether the learning process of
imputation and prediction could benefit from each other to achieve better
outcomes. And the question will be answered by studying the coexistence
patterns between missing points and observed ones in incomplete trajectories.
More specifically, the proposed model develops an imputation component based on
the self-attention mechanism to capture the coexistence patterns between
observations and missing points among encoder-decoder layers. Meanwhile, a
recurrent unit is integrated to extract the sequential embeddings from newly
imputed sequences for predicting the following location. Furthermore, a new
implementation called Imputation Cycle is introduced to enable gradual
imputation with prediction enhancement at multiple levels, which helps to
accelerate the speed of convergence. The experimental results on three
different real-world mobility datasets show that the proposed approach has
significant advantages over the competitive baselines across both imputation
and prediction tasks in terms of accuracy and stability.

Machine Learning