Investigating the accuracy of cross-learning time series forecasting methods

Artemios Anargyros Semenoglou, Evangelos Spiliotis, Spyros Makridakis, Vassilios Assimakopoulos

Research output: Contribution to journalArticlepeer-review


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 empirical evaluation, which is based on the M4 monthly data, confirms that cross-learning is a promising alternative to traditional forecasting, at least when appropriate strategies for extracting information from large, diverse time series data sets are considered. Ways of combining traditional with cross-learning methods are also examined in order to initiate further research in the field.

Original languageEnglish
Pages (from-to)1072-1084
Number of pages13
JournalInternational Journal of Forecasting
Issue number3
Publication statusPublished - 1 Jul 2021


  • Cross-learning
  • Features
  • M4 competition
  • Neural networks
  • Time series


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