TY - JOUR
T1 - Product sales probabilistic forecasting
T2 - An empirical evaluation using the M5 competition data
AU - Spiliotis, Evangelos
AU - Makridakis, Spyros
AU - Kaltsounis, Anastasios
AU - Assimakopoulos, Vassilios
N1 - Publisher Copyright:
© 2021
PY - 2021/10
Y1 - 2021/10
N2 - Supply chain management depends heavily on uncertain point forecasts of product sales. In order to deal with such uncertainty and optimize safety stock levels, methods that can estimate the right part of the sales distribution are required. Given the limited work that has been done in the field of probabilistic product sales forecasting, we propose and test some novel methods to estimate uncertainty, utilizing empirical computations and simulations to determine quantiles. To do so, we use the M5 competition data to empirically evaluate the forecasting and inventory performance of these methods by making comparisons both with established statistical approaches and advanced machine learning methods. Our results indicate that different methods should be employed based on the quantile of interest and the characteristics of the series being forecast, concluding that methods that employ relatively simple and faster to compute empirical estimations result in better inventory performance than more sophisticated and computer intensive approaches.
AB - Supply chain management depends heavily on uncertain point forecasts of product sales. In order to deal with such uncertainty and optimize safety stock levels, methods that can estimate the right part of the sales distribution are required. Given the limited work that has been done in the field of probabilistic product sales forecasting, we propose and test some novel methods to estimate uncertainty, utilizing empirical computations and simulations to determine quantiles. To do so, we use the M5 competition data to empirically evaluate the forecasting and inventory performance of these methods by making comparisons both with established statistical approaches and advanced machine learning methods. Our results indicate that different methods should be employed based on the quantile of interest and the characteristics of the series being forecast, concluding that methods that employ relatively simple and faster to compute empirical estimations result in better inventory performance than more sophisticated and computer intensive approaches.
KW - Empirical evaluation
KW - M5 competition
KW - Probabilistic forecasting
KW - Sales forecasting
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85111065825&partnerID=8YFLogxK
U2 - 10.1016/j.ijpe.2021.108237
DO - 10.1016/j.ijpe.2021.108237
M3 - Article
AN - SCOPUS:85111065825
SN - 0925-5273
VL - 240
JO - International Journal of Production Economics
JF - International Journal of Production Economics
M1 - 108237
ER -