Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos, Zhi Chen, Anil Gaba, Ilia Tsetlin, Robert L. Winkler
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. © 2021 The Author(s)
Institute For the Future, University of Nicosia, Ringgold ID 121343, Cyprus; Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Greece; Department of Analytics and Operations, NUS Business School, National University of Singapore, Ringgold ID 37580, 119245, Singapore; INSEAD, Ringgol ID 52160, 138676, Singapore; Fuqua School of Business, Duke University, Ringgold ID 33853, Durham, 27708, NC, United States
