  Time series forecasting is a relevant task that is performed in several
real-world scenarios such as product sales analysis and prediction of energy
demand. Given their accuracy performance, currently, Recurrent Neural Networks
(RNNs) are the models of choice for this task. Despite their success in time
series forecasting, less attention has been paid to make the RNNs trustworthy.
For example, RNNs can not naturally provide an uncertainty measure to their
predictions. This could be extremely useful in practice in several cases e.g.
to detect when a prediction might be completely wrong due to an unusual pattern
in the time series. Whittle Sum-Product Networks (WSPNs), prominent deep
tractable probabilistic circuits (PCs) for time series, can assist an RNN with
providing meaningful probabilities as uncertainty measure. With this aim, we
propose RECOWN, a novel architecture that employs RNNs and a discriminant
variant of WSPNs called Conditional WSPNs (CWSPNs). We also formulate a
Log-Likelihood Ratio Score as better estimation of uncertainty that is tailored
to time series and Whittle likelihoods. In our experiments, we show that
RECOWNs are accurate and trustworthy time series predictors, able to "know when
they do not know".
