Research and Application of Traffic Forecasting in Customer Service Center Based on ARIMA Model and LSTM Neural Network Model. Issue 3 (April 2021)

• Record Type: Journal Article
• Authors: Zhang, Mingjie; Sheng, Yan; Tian, Nuo; Liu, Wei; Wang, Hui; Zhu, Longzhu; Xu, Qing
• Is Part Of: Journal of physics. Volume 1881:Issue 3(2021)
• Publication Date: 2021-04
• Subjects: Traffic Volume -- ARIMA Model -- LSTM Neural Network; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1881/3/032063
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Traffic data is the premise of the number of call center seats. The corresponding agents can be arranged for different traffic volumes to achieve optimal configuration of call center human resources. In this paper, ARIMA model and LSTM neural network model based on time series are used to predict traffic. The traffic of the power call center in Hebei Province is taken as an example to conduct experiments on Python software. The results show that LSTM neural network model has higher prediction accuracy than ARIMA model.