  Time series prediction has been studied in a variety of domains. However, it
is still challenging to predict future series given historical observations and
past exogenous data. Existing methods either fail to consider the interactions
among different components of exogenous variables which may affect the
prediction accuracy, or cannot model the correlations between exogenous data
and target data. Besides, the inherent temporal dynamics of exogenous data are
also related to the target series prediction, and thus should be considered as
well. To address these issues, we propose an end-to-end deep learning model,
i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which
incorporates spatio-temporal feature extraction of exogenous variables and
temporal dynamics modeling of target variables into a single framework.
Moreover, by introducing the hierarchical attention mechanism, HRHN can
adaptively select the relevant exogenous features in different semantic levels.
We carry out comprehensive empirical evaluations with various methods over
several datasets, and show that HRHN outperforms the state of the arts in time
series prediction, especially in capturing sudden changes and sudden
oscillations of time series.
