Nguyen, H.D., Tran, K.P., Thomassey, S. and Hamad, M. (2021) Forecasting and Anomaly Detection Approaches Using LSTM and LSTM Autoencoder Techniques with the Applications in Supply Chain Management. International Journal of Information Management, 57, Article ID: 102282.
https://doi.org/10.1016/j.ijinfomgt.2020.102282

has been cited by the following article:

• TITLE: Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

  AUTHORS: Shifeng Ni, Yan Peng, Zijian Liu

  KEYWORDS: Data Analysis, Bi-LSTM, Fresh Food E-Commerce, Logistics Demand Forecast

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.9, September 19, 2022

  ABSTRACT: Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.