Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs
Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs
复制标题
集成格兰杰因果关系和向量自回归进行大规模 WLAN 的流量预测
DOI：
10.3837/tiis.2016.01.008
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发表时间：
2016-01-31
影响因子：
1.5
通讯作者：
Cui, Songyue
中科院分区：
文献类型：
--
作者：
Lu, Zheng;Zhou, Chen;Cui, Songyue
灵活的大规模无线局域网被广泛部署在校园、机场、商场、公司等人员密集、移动的场所，但由于链路间干扰和吞吐量的高度不均匀性，给大规模无线局域网的网络管理带来困难。因此，流量很难准确预测。本文通过对两个真实的大规模无线局域网的流量分析，发现了两种情况下的格兰杰因果关系。结合信息熵分析表明，考虑格兰杰因果关系的目标AP流量预测比单独利用目标AP或考虑不相关AP的流量预测更具有可预测性。因此，本文提出了一种新的预测方法--格兰杰因果关系和向量自回归（GCVAR）方法，该方法在向量自回归（VAR）的基础上，考虑了接入点序列共享格兰杰因果关系，对两种真实的场景下的流量进行预测，从而去除了多变量时间序列引入的冗余和噪声。实验表明，GCVAR比传统的单变量时间序列（如ARIMA，WARIMA）更有效。特别是，GCVAR消耗两个数量级以下的ARIMA/WARIMA造成的。
Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method -Granger Causality and Vector Auto-Regression (GCVAR), which takes APs series sharing Granger Causality based on Vector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.