Probabilistic Time Series Forecasting with Implicit Quantile Networks

Importantly, our approach does not make Here, we propose a general method for probabilistic any a-priori assumptions on the underlying distribution of time series forecasting. We combine an our data. The probabilistic output of our model is generated autoregressive recurrent neural network to model via Implicit Quantile Networks (Dabney et al., 2018) temporal dynamics with Implicit Quantile Networks (IQN) and is trained by minimizing the integrand of the to learn a large class of distributions over a Continuous Ranked Probability Score (CRPS) (Matheson & time-series target. When compared to other probabilistic Winkler, 1976).