  Forecasting models that are trained across sets of many time series, known as
Global Forecasting Models (GFM), have shown recently promising results in
forecasting competitions and real-world applications, outperforming many
state-of-the-art univariate forecasting techniques. In most cases, GFMs are
implemented using deep neural networks, and in particular Recurrent Neural
Networks (RNN), which require a sufficient amount of time series to estimate
their numerous model parameters. However, many time series databases have only
a limited number of time series. In this study, we propose a novel, data
augmentation based forecasting framework that is capable of improving the
baseline accuracy of the GFM models in less data-abundant settings. We use
three time series augmentation techniques: GRATIS, moving block bootstrap
(MBB), and dynamic time warping barycentric averaging (DBA) to synthetically
generate a collection of time series. The knowledge acquired from these
augmented time series is then transferred to the original dataset using two
different approaches: the pooled approach and the transfer learning approach.
When building GFMs, in the pooled approach, we train a model on the augmented
time series alongside the original time series dataset, whereas in the transfer
learning approach, we adapt a pre-trained model to the new dataset. In our
evaluation on competition and real-world time series datasets, our proposed
variants can significantly improve the baseline accuracy of GFM models and
outperform state-of-the-art univariate forecasting methods.
