{"id":6165,"date":"2026-05-10T06:32:41","date_gmt":"2026-05-09T22:32:41","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6165"},"modified":"2026-07-03T15:28:47","modified_gmt":"2026-07-03T07:28:47","slug":"seasonal-forecasting-of-mobile-data-traffic-in-gsm-networks-with-linear-trend","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=seasonal-forecasting-of-mobile-data-traffic-in-gsm-networks-with-linear-trend","title":{"rendered":"Seasonal forecasting of mobile data traffic in GSM networks with linear trend"},"content":{"rendered":"\n
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<\/i> 2020<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 23, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

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V. Sathyendra Kumar1<\/sup><\/i><\/a> and A. Muthukumaravel2<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Master of Computer Applications, Annamacharya Institute of Technology and Sciences, Rajampet, India<\/p>\n\n\n\n

2<\/sup>Faculty of Arts & Science, Bharath Institute of Higher Education Research, Chennai, India<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0February 24, 2020
Accepted:\u00a0April 30, 2020
Publication Date:\u00a0May 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Seasonal Data of Users with Linear Trend.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202009_23(3).0011<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Recently, the popularity of predictive analytics has grown in many areas. For mobile networks, it is the most critical technique that can offer the advantages of mobile network planning to operators for predicting the mobile traffic of each Long Term Evolution (LTE) market. It can help operators spend the least investment on new sites and new communities but can guarantee an excellent service experience for mobile broadband users. Mobility management essentially has two databases: one is the Home Location Register (HLR) and the other the Visitor Location Register (VLR) is. The mobile user can move to call tracking from anywhere in the network location registry. In mobile data networks, an essential factor is a demand for telecommunications containing the number of subscribers and the prices for the required service data. To understand what subscribers need to build customer satisfaction, this requirement needs to be precisely predicted. In this context, the approach to forecasting mobile telephony used in Indian telecommunications, in our view, does not fully take into account the demand for data already recorded on its core network. In this paper, we are analyzing the Seasonal Forecast Time series model (SFT). This approach is used in this paper to first analyze the transferred data traffic from the core network of the operator to find a suitable model describing the inherent characteristics of data traffic and the use of model for future predictive loading in VLR database.<\/p>\n\n\n\n

Keywords:\u00a0Mobile Data Traffic; Seasonality forecasting; Time series analysis; HLR-VLR databases<\/em><\/p>\n\n\n\n

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