Research Article
Short Term Prediction Models of Mobile Network Traffic Based on Time Series Analysis
@INPROCEEDINGS{10.1007/978-3-319-73564-1_20, author={Yunxue Gao and Liming Zheng and Donglai Zhao and Yue Wu and Gang Wang}, title={Short Term Prediction Models of Mobile Network Traffic Based on Time Series Analysis}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Traffic model Multiplicative seasonal ARIMA Holt-Winters Short term prediction}, doi={10.1007/978-3-319-73564-1_20} }- Yunxue Gao
Liming Zheng
Donglai Zhao
Yue Wu
Gang Wang
Year: 2018
Short Term Prediction Models of Mobile Network Traffic Based on Time Series Analysis
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_20
Abstract
In the mobile network, building a prediction based network traffic model is of great significance for mobile network optimization, so that the operators is able to schedule the resources adaptively. In the paper, multiplicative seasonal Autoregressive Integrated Moving Average model (ARIMA) and Holt-Winters model are proposed for modeling of traffic predication, where the historical traffic series of a typical tourist area are utilized to verify the performance. The two methods analyze the trend of mobile network traffic per hour, build and validate models. Then predict mobile network traffic within a given period of time. The error rate of different models predictions is analyzed to provide certain decision basis for the allocation of network resources.
