北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (s1): 10-14.doi: 10.13190/j.jbupt.2017.s.003

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基于S-ARIMA模型的无线通信网络业务量预测方法

李文璟, 陈晨, 喻鹏, 熊翱   

  1. 北京邮电大学 网络与交换技术国家重点实验室, 北京 100876
  • 收稿日期:2016-09-15 出版日期:2017-09-28 发布日期:2017-09-28
  • 作者简介:李文璟(1973-),女,教授,研究生导师,Email:wjli@bupt.edu.cn.
  • 基金资助:
    国家高技术研究发展计划(863计划)项目(2014AA01A701);国家自然科学基金项目(61271187)

Traffic Prediction for Wireless Communication Networks Using S-ARIMA Model

LI Wen-jing, CHEN Chen, YU Peng, XIONG Ao   

  1. State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2016-09-15 Online:2017-09-28 Published:2017-09-28

摘要: 针对当前业务量预测方法过于理想化、预测准确度不高等问题,根据现网业务量特征提出了一种基于乘积季节自回归求和移动平均(S-ARIMA)模型的业务量预测方法. 依据现网业务量的特征,详细分析了基于S-ARIMA的业务量预测建模的数学过程,经过现网大量业务量数据验证,S-ARIMA模型相比其他模型方法在预测值和置信区间上均具有较好的结果,是一种合理有效的业务量预测方法.

关键词: S-ARIMA模型, 时间序列, 无线通信网, 业务量预测

Abstract: During the study of the technologies of energy saving, how to ensure the changing trend of traffic accurately is a prerequisite of many energy-saving technology. Contraposing the current methods for traffic prediction being a bit idealistic and the low accuracy prediction, we propose a traffic prediction method based on the seasonal autoregressive integrated moving average(S-ARIMA) model in view of the traffic character in the network and implement it. According to the characteristics of the traffic character in the network, We analyze the mathematical process of the S-ARIMA mode detailedly. It is tested by a lot of traffic data in the wireless communication networks and the results indicate that for prediction values and confidence intervals S-ARIMA model performs better than other models. Therefore, this traffic prediction for wireless communication networks using S-ARIMA model is reasonable and efficient.

Key words: seasonal autoregressive integrated moving average, time series, wireless communication networks, traffic prediction

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