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Nie, L.S., Jiang, D.D., Yu, S. and Song, H.B. (2017) Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks. 2017 IEEE Wireless Communications and Networking Conference, San Francisco, 19-22 March 2017, 1-5.
https://doi.org/10.1109/WCNC.2017.7925498

has been cited by the following article:

• TITLE: Smart Network Price Policy for ISP Based on Traffic Prediction

  AUTHORS: Tingya Su

  KEYWORDS: Time-Series Analysis, Network Traffic Prediction, Telecommunication Traffic, ARIMA, LSTM, Deep Learning, ISP Pricing

  JOURNAL NAME: Journal of Mathematical Finance, Vol.11 No.1, February 4, 2021

  ABSTRACT: The explosion of traffic brings the challenges for Internet Service Providers (ISPs) to make a profit with the high cost of infrastructure and increased competition. This calls for economic mechanisms that can enable providers to allocate on-demand resources through the prediction of traffic volumes and adjust the price. In this paper, we analyze the network traffic pattern of mobile data and make an accurate prediction of traffic volumes through ARIMA and LSTM. Based on the analysis, we then suggest a scalable price strategy for ISPs to satisfy the various requirements of customers.