TY - JOUR
T1 - The M5 competition
T2 - Background, organization, and implementation
AU - Makridakis, Spyros
AU - Spiliotis, Evangelos
AU - Assimakopoulos, Vassilios
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021
Y1 - 2021
N2 - The M5 competition follows the previous four M competitions, whose purpose is to learn from empirical evidence how to improve forecasting performance and advance the theory and practice of forecasting. M5 focused on a retail sales forecasting application with the objective to produce the most accurate point forecasts for 42,840 time series that represent the hierarchical unit sales of the largest retail company in the world, Walmart, as well as to provide the most accurate estimates of the uncertainty of these forecasts. Hence, the competition consisted of two parallel challenges, namely the Accuracy and Uncertainty forecasting competitions. M5 extended the results of the previous M competitions by: (a) significantly expanding the number of participating methods, especially those in the category of machine learning; (b) evaluating the performance of the uncertainty distribution along with point forecast accuracy; (c) including exogenous/explanatory variables in addition to the time series data; (d) using grouped, correlated time series; and (e) focusing on series that display intermittency. This paper describes the background, organization, and implementations of the competition, and it presents the data used and their characteristics. Consequently, it serves as introductory material to the results of the two forecasting challenges to facilitate their understanding.
AB - The M5 competition follows the previous four M competitions, whose purpose is to learn from empirical evidence how to improve forecasting performance and advance the theory and practice of forecasting. M5 focused on a retail sales forecasting application with the objective to produce the most accurate point forecasts for 42,840 time series that represent the hierarchical unit sales of the largest retail company in the world, Walmart, as well as to provide the most accurate estimates of the uncertainty of these forecasts. Hence, the competition consisted of two parallel challenges, namely the Accuracy and Uncertainty forecasting competitions. M5 extended the results of the previous M competitions by: (a) significantly expanding the number of participating methods, especially those in the category of machine learning; (b) evaluating the performance of the uncertainty distribution along with point forecast accuracy; (c) including exogenous/explanatory variables in addition to the time series data; (d) using grouped, correlated time series; and (e) focusing on series that display intermittency. This paper describes the background, organization, and implementations of the competition, and it presents the data used and their characteristics. Consequently, it serves as introductory material to the results of the two forecasting challenges to facilitate their understanding.
KW - Accuracy
KW - Forecasting competitions
KW - M competitions
KW - Retail sales forecasting
KW - Time series
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85115293157&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2021.07.007
DO - 10.1016/j.ijforecast.2021.07.007
M3 - Article
AN - SCOPUS:85115293157
SN - 0169-2070
JO - International Journal of Forecasting
JF - International Journal of Forecasting
ER -