  Recurrent neural networks (RNNs) are state-of-the-art in several sequential
learning tasks, but they often require considerable amounts of data to
generalise well. For many time series forecasting (TSF) tasks, only a few
dozens of observations may be available at training time, which restricts use
of this class of models. We propose a novel RNN-based model that directly
addresses this problem by learning a shared feature embedding over the space of
many quantised time series. We show how this enables our RNN framework to
accurately and reliably forecast unseen time series, even when there is little
to no training data available.
