TY - JOUR
T1 - M5 accuracy competition
T2 - Results, findings, and conclusions
AU - Makridakis, Spyros
AU - Spiliotis, Evangelos
AU - Assimakopoulos, Vassilios
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2022
Y1 - 2022
N2 - In this study, we present the results of the M5 “Accuracy” competition, which was the first of two parallel challenges in the latest M competition with the aim of advancing the theory and practice of forecasting. The main objective in the M5 “Accuracy” competition was to accurately predict 42,840 time series representing the hierarchical unit sales for the largest retail company in the world by revenue, Walmart. The competition required the submission of 30,490 point forecasts for the lowest cross-sectional aggregation level of the data, which could then be summed up accordingly to estimate forecasts for the remaining upward levels. We provide details of the implementation of the M5 “Accuracy” challenge, as well as the results and best performing methods, and summarize the major findings and conclusions. Finally, we discuss the implications of these findings and suggest directions for future research.
AB - In this study, we present the results of the M5 “Accuracy” competition, which was the first of two parallel challenges in the latest M competition with the aim of advancing the theory and practice of forecasting. The main objective in the M5 “Accuracy” competition was to accurately predict 42,840 time series representing the hierarchical unit sales for the largest retail company in the world by revenue, Walmart. The competition required the submission of 30,490 point forecasts for the lowest cross-sectional aggregation level of the data, which could then be summed up accordingly to estimate forecasts for the remaining upward levels. We provide details of the implementation of the M5 “Accuracy” challenge, as well as the results and best performing methods, and summarize the major findings and conclusions. Finally, we discuss the implications of these findings and suggest directions for future research.
KW - Accuracy
KW - Forecasting competitions
KW - M competitions
KW - Machine learning
KW - Retail sales forecasting
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85122627187&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2021.11.013
DO - 10.1016/j.ijforecast.2021.11.013
M3 - Article
AN - SCOPUS:85122627187
SN - 0169-2070
JO - International Journal of Forecasting
JF - International Journal of Forecasting
ER -