LSTM with particle Swam optimization for sales forecasting

作者：

Highlights：

• Propose a sale forecasting approach based on LSTM with PSO for E-commerce companies.

• The number of hidden neurons and iterations in LSTM are optimized by PSO.

• We compare the proposed approach with 9 competing approaches.

• Evaluated on the real datasets from an E-commerce company and 3 benchmark datasets.

• Proposed models achieved good results in forecasting accuracy.

摘要

•Propose a sale forecasting approach based on LSTM with PSO for E-commerce companies.•The number of hidden neurons and iterations in LSTM are optimized by PSO.•We compare the proposed approach with 9 competing approaches.•Evaluated on the real datasets from an E-commerce company and 3 benchmark datasets.•Proposed models achieved good results in forecasting accuracy.

论文关键词：Long short-term memory,Particle swam optimization,Sales forecasting,Time series,E-commerce

论文评审过程：Received 30 June 2020, Revised 25 December 2021, Accepted 3 January 2022, Available online 7 January 2022, Version of Record 12 January 2022.

论文官网地址：https://doi.org/10.1016/j.elerap.2022.101118