基于粗数据的细粒度网络流量预测
Fine-grained network traffic prediction from coarse data
摘要：
信息和通信技术系统提供关于计算机网络流量的详细信息。然而，由于存储限制，关于过去流量的一些信息通常仅以聚合形式保留。在本文中，我们表明，线性高斯状态空间模型产生简单而有效的方法，使预测的基础上，时间序列在不同的聚合水平。这些模型将粗粒度和细粒度的时间序列链接到能够提供细粒度预测的单个模型。我们的数值实验表明，在预期的平均绝对预测误差提高了3.7倍时，预测使用，而不是忽略，额外的粗粒度观测。的预测中获得的贝叶斯公式的模型，它允许提供的交通预测服务，从粗粒度的历史数据中获得的高度翔实的先验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
