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.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant No. 61401120.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gao, Y., Zheng, L., Zhao, D., Wu, Y., Wang, G. (2018). Short Term Prediction Models of Mobile Network Traffic Based on Time Series Analysis. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_20
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DOI: https://doi.org/10.1007/978-3-319-73564-1_20
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