  Despite the high performance of neural network-based time series forecasting
methods, the inherent challenge in explaining their predictions has limited
their applicability in certain application areas. Due to the difficulty in
identifying causal relationships between the input and output of such black-box
methods, they rarely have been adopted in domains such as legal and medical
fields in which the reliability and interpretability of the results can be
essential. In this paper, we propose \model, a novel deep learning-based
probabilistic time series forecasting architecture that is intrinsically
interpretable. We conduct experiments with multiple datasets and performance
metrics and empirically show that our model is not only interpretable but also
provides comparable performance to state-of-the-art probabilistic time series
forecasting methods. Furthermore, we demonstrate that interpreting the
parameters of the stochastic processes of interest can provide useful insights
into several application areas.
