The M4 competition identified innovative forecasting methods, advancing the theory and
practice of forecasting. One of the most promising innovations of M4 was the utilization of
cross-learning approaches that allow models to learn from multiple series how to accurately
predict individual ones. In this paper, we investigate the potential of cross-learning by
developing various neural network models that adopt such an approach, and we compare
their accuracy to that of traditional models that are trained in a series-by-series fashion. Our …