  We propose a framework for general probabilistic multi-step time series
regression. Specifically, we exploit the expressiveness and temporal nature of
Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional
structures), the nonparametric nature of Quantile Regression and the efficiency
of Direct Multi-Horizon Forecasting. A new training scheme,
*forking-sequences*, is designed for sequential nets to boost stability and
performance. We show that the approach accommodates both temporal and static
covariates, learning across multiple related series, shifting seasonality,
future planned event spikes and cold-starts in real life large-scale
forecasting. The performance of the framework is demonstrated in an application
to predict the future demand of items sold on Amazon.com, and in a public
probabilistic forecasting competition to predict electricity price and load.
