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Viedma, D.T., Rivas, A.J.R., Ojeda, F.C. and Jesus Díaz, M.J.D. (2018) An Approximation to Deep Learning Touristic-Related Time Series Forecasting. In: Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A., Eds., Intelligent Data Engineering and Automated Learning—IDEAL 2018. Lecture Notes in Computer Science, Vol. 11314, Springer, Cham, 448-456. https://doi.org/10.1007/978-3-030-03493-1_47

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• TITLE: Research on E-Commerce Inventory Demand Forecasting Based on NAR Neural Network

  AUTHORS: Peijian Wu, Ge Zhang, Yuqing Li, Xuqin Chen

  KEYWORDS: E-Commerce Enterprises, NAR Neural Network, Inventory Demand, Forecasting

  JOURNAL NAME: Open Access Library Journal, Vol.10 No.5, May 24, 2023

  ABSTRACT: As the competition of e-commerce enterprises intensifies, efficient demand supply becomes an important weight for enterprises to compete. The current inaccuracy of demand forecasting in e-commerce enterprises is frequent, leading to increased difficulty in inventory management and weakened competitiveness. In order to improve the accuracy of demand forecasting, intelligent decision-making technology is introduced to establish a NAR neural network model, and the NAR model is used to simulate the prediction of historical sales data in the current system. The simulation results are compared with the prediction results of the AR model, and it is concluded that the prediction results of the NAR neural network are more accurate and better for enterprises to make inventory demand plans and accelerate the transformation of e-commerce enterprises to digital intelligence.