A Multi-Horizon Quantile Recurrent Forecaster

Understand

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.