TY - GEN
T1 - A Selection of Advanced Technologies for Demand Forecasting in the Retail Industry
AU - Wang, Jiaxing
AU - Liu, G. Q.
AU - Liu, Lu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/10
Y1 - 2019/5/10
N2 - Retail companies always attempt to find a forecasting method to balance their purchasing and sales, whereas the performances of various prediction techniques are still not reliable. Furthermore, it is a question that how to select the proper forecasting model for some specific type of products. In this research, the classical forecasting models and the latest developing forecasting technologies are compared together based on the perishable products and non-perishable items respectively. The process is designed to compare the performance of typical statistic methods with several machine learning methods based on the thousands of historical transaction record of a large grocery retailer. The criterion is also explored in this study include predictive performance, generalization ability, runtime, cost and convenience to evaluate the comprehensive performance of these models, thus companies can easily choose their most accepted model.
AB - Retail companies always attempt to find a forecasting method to balance their purchasing and sales, whereas the performances of various prediction techniques are still not reliable. Furthermore, it is a question that how to select the proper forecasting model for some specific type of products. In this research, the classical forecasting models and the latest developing forecasting technologies are compared together based on the perishable products and non-perishable items respectively. The process is designed to compare the performance of typical statistic methods with several machine learning methods based on the thousands of historical transaction record of a large grocery retailer. The criterion is also explored in this study include predictive performance, generalization ability, runtime, cost and convenience to evaluate the comprehensive performance of these models, thus companies can easily choose their most accepted model.
KW - ARIMA
KW - Deep Learning
KW - Demand Forecasting
KW - Recurrent neural networks (RNN)
KW - Retail Industry
UR - http://www.scopus.com/inward/record.url?scp=85066628647&partnerID=8YFLogxK
U2 - 10.1109/ICBDA.2019.8713196
DO - 10.1109/ICBDA.2019.8713196
M3 - Conference Proceeding
AN - SCOPUS:85066628647
T3 - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
SP - 317
EP - 320
BT - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Y2 - 15 March 2019 through 18 March 2019
ER -