  Heterogeneity and irregularity of multi-source data sets present a
significant challenge to time-series analysis. In the literature, the fusion of
multi-source time-series has been achieved either by using ensemble learning
models which ignore temporal patterns and correlation within features or by
defining a fixed-size window to select specific parts of the data sets. On the
other hand, many studies have shown major improvement to handle the
irregularity of time-series, yet none of these studies has been applied to
multi-source data. In this work, we design a novel architecture, PIETS, to
model heterogeneous time-series. PIETS has the following characteristics: (1)
irregularity encoders for multi-source samples that can leverage all available
information and accelerate the convergence of the model; (2) parallelised
neural networks to enable flexibility and avoid information overwhelming; and
(3) attention mechanism that highlights different information and gives high
importance to the most related data. Through extensive experiments on
real-world data sets related to COVID-19, we show that the proposed
architecture is able to effectively model heterogeneous temporal data and
outperforms other state-of-the-art approaches in the prediction task.
