论文标题
细粒度的网络流量从粗数据预测
Fine-grained network traffic prediction from coarse data
论文作者
论文摘要
ICT系统提供了有关计算机网络流量的详细信息。但是，由于存储限制，有关过去流量的某些信息通常仅以汇总形式保留。在本文中，我们表明 线性高斯状态空间模型产生了简单而有效的方法，以根据不同聚合水平的时间序列进行预测。这些模型将粗粒和细粒度的时间序列与能够提供细粒预测的单个模型联系起来。当对预测的预测绝对预测误差的3.7倍提高时，我们的数值实验是使用预测而不是忽略附加的粗粒观测的3.7倍。预测是在模型的贝叶斯公式中获得的，该模型允许通过从粗粒的历史数据获得的高度信息的先验来提供交通预测服务。
ICT systems provide detailed information on computer network traffic. However, due to storage limitations, some of the information on past traffic is often only retained in an aggregated form. In this paper we show that Linear Gaussian State Space Models yield simple yet effective methods to make predictions based on time series at different aggregation levels. The models link coarse-grained and fine-grained time series to a single model that is able to provide fine-grained predictions. Our numerical experiments show up to 3.7 times improvement in expected mean absolute forecast error when forecasts are made using, instead of ignoring, additional coarse-grained observations. The forecasts are obtained in a Bayesian formulation of the model, which allows for provisioning of a traffic prediction service with highly informative priors obtained from coarse-grained historical data.
