Traffic Forecasting of Core Network Based on Improved Logistic Regression

Song Xin, Xu Yuanbiao, Zhang Qijia, Lai Zhimao, Feng Renhai
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引用次数: 2

Abstract

Traffic forecasting of core network plays an important role in network planning, traffic management, etc. Therefore, a predictive model that can accurately predict core network traffic is needed. This article proposes a new traffic forecasting method of core network based on logistic regression (LR). In order to get an accurate logistic model, new LR parameter estimation algorithm is proposed. First, the unknown parameters of LR are replaced by the minimum variance unbiased estimator to ensure the accuracy. In order to reduce the computational complexity, a statistical model of LR is introduced. Then, the unknown parameters of the LR are estimated based on the Cramer-Rao lower bound, and then the LR is further obtained based on the proposed estimator. Finally, the accuracy of the model is verified through experiments based on traffic data of core network. Experimental result shows that the improved logistic model proposed in this paper is more accurate than other methods.
基于改进逻辑回归的核心网流量预测
核心网流量预测在网络规划、流量管理等方面发挥着重要作用。因此,需要一种能够准确预测核心网流量的预测模型。提出了一种基于逻辑回归的核心网流量预测新方法。为了得到准确的逻辑模型,提出了一种新的LR参数估计算法。首先,用最小方差无偏估计量代替LR的未知参数,以保证精度;为了降低计算复杂度,引入了LR的统计模型。然后,基于Cramer-Rao下界对LR的未知参数进行估计,然后基于所提出的估计量进一步得到LR。最后,通过基于核心网流量数据的实验验证了模型的准确性。实验结果表明,本文提出的改进逻辑模型比其他方法更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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