Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

@article{Xu2016BigDD,
  title={Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach},
  author={Fengli Xu and Yu-Yun Lin and Jiaxin Huang and Di Wu and Hongzhi Shi and Jeungeun Song and Yong Li},
  journal={IEEE Transactions on Services Computing},
  year={2016},
  volume={9},
  pages={796-805}
}
Understanding and forecasting mobile traffic of large scale cellular networks is extremely valuable for service providers to control and manage the explosive mobile data, such as network planning, load balancing, and data pricing mechanisms. This paper targets at extracting and modeling traffic patterns of 9,000 cellular towers deployed in a metropolitan city. To achieve this goal, we design, implement, and evaluate a time series analysis approach that is able to decompose large scale mobile… CONTINUE READING
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