TY - JOUR
T1 - Investigating the accuracy of cross-learning time series forecasting methods
AU - Semenoglou, Artemios Anargyros
AU - Spiliotis, Evangelos
AU - Makridakis, Spyros
AU - Assimakopoulos, Vassilios
N1 - Publisher Copyright:
© 2020 International Institute of Forecasters
PY - 2021/7/1
Y1 - 2021/7/1
N2 - 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.
AB - 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.
KW - Cross-learning
KW - Features
KW - M4 competition
KW - Neural networks
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85098585860&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2020.11.009
DO - 10.1016/j.ijforecast.2020.11.009
M3 - Article
AN - SCOPUS:85098585860
SN - 0169-2070
VL - 37
SP - 1072
EP - 1084
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 3
ER -