Prophet model and Gaussian process regression based user traffic prediction in wireless networks

User traffic prediction is an important topic for wireless network operators. A user traffic prediction method based on Prophet and Gaussian process regression is proposed in this paper. The proposed method first employs discrete wavelet transform to decompose the user traffic time series to high-frequency component and low-frequency component. The low-frequency component bears the long-range dependence of user network traffic, while the high-frequency component reveals the gusty and irregular fluctuations of user network traffic. Then Prophet model and Gaussian process regression are applied to predict the two components respectively based on the characteristics of the two components. Experimental results demonstrate that the proposed model outperforms the existing time series prediction method.

[1]  Xinlei Chen,et al.  Characterizing and Predicting Individual Traffic Usage of Mobile Application in Cellular Network , 2018, UbiComp/ISWC Adjunct.

[2]  Meng Xu,et al.  Hybrid holiday traffic predictions in cellular networks , 2018, NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium.

[3]  Sebastian Göndör,et al.  Optimizing the Power Consumption of Mobile Networks Based on Traffic Prediction , 2014, 2014 IEEE 38th Annual Computer Software and Applications Conference.

[4]  Benjamin Letham,et al.  Forecasting at Scale , 2018, PeerJ Prepr..

[5]  Bao-Shuh Paul Lin,et al.  Applying Big Data, Machine Learning, and SDN/NFV to 5G Traffic Clustering, Forecasting, and Management , 2018, 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft).

[6]  Xiang Cheng,et al.  Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks , 2018, IEEE Internet of Things Journal.

[7]  Lei Shi,et al.  A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction , 2020, IEEE Transactions on Network Science and Engineering.

[8]  Bao-Shuh Paul Lin,et al.  A practical model for traffic forecasting based on big data, machine-learning, and network KPIs , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[9]  Chi-Hua Chen,et al.  Mobile Data Usage Prediction System and Method , 2017, 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[10]  Yong Li,et al.  Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach , 2016, IEEE Transactions on Services Computing.

[11]  Shui Yu,et al.  Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Wei Heng,et al.  Base station sleeping mechanism based on traffic prediction in heterogeneous networks , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[13]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[14]  Janne Riihijärvi,et al.  Machine Learning for Performance Prediction in Mobile Cellular Networks , 2018, IEEE Computational Intelligence Magazine.

[15]  Zhihan Lv,et al.  A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic , 2019, Int. J. Commun. Syst..

[16]  Benjamin Letham,et al.  Forecasting at Scale , 2018 .

[17]  Jennifer C. Hou,et al.  Characterizing individual user behaviors in wlans , 2007, MSWiM '07.

[18]  Fang Liu,et al.  Characterizing User Behavior in Mobile Internet , 2015, IEEE Transactions on Emerging Topics in Computing.