Short Term Prediction Models of Mobile Network Traffic Based on Time Series Analysis

2018 ◽  
pp. 205-211
Author(s):  
Yunxue Gao ◽  
Liming Zheng ◽  
Donglai Zhao ◽  
Yue Wu ◽  
Gang Wang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Network Traffic ◽  
Prediction Models ◽  
Mobile Network ◽  
Short Term ◽  
Series Analysis ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Short Term Prediction of PM10Concentrations Using Seasonal Time Series Analysis

2016 ◽  
Vol 47 ◽  
pp. 05001 ◽  
Author(s):  
Hazrul Abdul Hamid ◽  
Ahmad Shukri Yahaya ◽  
Nor Azam Ramli ◽  
Ahmad Zia Ul-Saufie ◽  
Mohd Norazam Yasin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Short Term ◽  
Series Analysis ◽  
Seasonal Time Series ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sign in / Sign up

Export Citation Format

Share Document