Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks

Understand

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.