  Here, we propose a general method for probabilistic time series forecasting.
We combine an autoregressive recurrent neural network to model temporal
dynamics with Implicit Quantile Networks to learn a large class of
distributions over a time-series target. When compared to other probabilistic
neural forecasting models on real- and simulated data, our approach is
favorable in terms of point-wise prediction accuracy as well as on estimating
the underlying temporal distribution.
