TY - JOUR
T1 - The M5 uncertainty competition
T2 - Results, findings and conclusions
AU - Makridakis, Spyros
AU - Spiliotis, Evangelos
AU - Assimakopoulos, Vassilios
AU - Chen, Zhi
AU - Gaba, Anil
AU - Tsetlin, Ilia
AU - Winkler, Robert L.
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2021
Y1 - 2021
N2 - This paper describes the M5 “Uncertainty” competition, the second of two parallel challenges of the latest M competition, aiming to advance the theory and practice of forecasting. The particular objective of the M5 “Uncertainty” competition was to accurately forecast the uncertainty distributions of the realized values of 42,840 time series that represent the hierarchical unit sales of the largest retail company in the world by revenue, Walmart. To do so, the competition required the prediction of nine different quantiles (0.005, 0.025, 0.165, 0.250, 0.500, 0.750, 0.835, 0.975, and 0.995), that can sufficiently describe the complete distributions of future sales. The paper provides details on the implementation and execution of the M5 “Uncertainty” competition, presents its results and the top-performing methods, and summarizes its major findings and conclusions. Finally, it discusses the implications of its findings and suggests directions for future research.
AB - This paper describes the M5 “Uncertainty” competition, the second of two parallel challenges of the latest M competition, aiming to advance the theory and practice of forecasting. The particular objective of the M5 “Uncertainty” competition was to accurately forecast the uncertainty distributions of the realized values of 42,840 time series that represent the hierarchical unit sales of the largest retail company in the world by revenue, Walmart. To do so, the competition required the prediction of nine different quantiles (0.005, 0.025, 0.165, 0.250, 0.500, 0.750, 0.835, 0.975, and 0.995), that can sufficiently describe the complete distributions of future sales. The paper provides details on the implementation and execution of the M5 “Uncertainty” competition, presents its results and the top-performing methods, and summarizes its major findings and conclusions. Finally, it discusses the implications of its findings and suggests directions for future research.
KW - Forecasting competitions
KW - M competitions
KW - Machine learning
KW - Probabilistic forecasts
KW - Retail sales forecasting
KW - Time series
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85112504527&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2021.10.009
DO - 10.1016/j.ijforecast.2021.10.009
M3 - Article
AN - SCOPUS:85112504527
SN - 0169-2070
JO - International Journal of Forecasting
JF - International Journal of Forecasting
ER -