Investigating the accuracy of cross-learning time series forecasting methods

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

International Journal of Forecasting, 2021, vol. 37, issue 3, 1072-1084

Abstract: 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.

Keywords: Time series; Cross-learning; Features; Neural networks; M4 competition (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:3:p:1072-1084

DOI: 10.1016/j.ijforecast.2020.11.009

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