Fine-grained network traffic prediction from coarse data

TL;DR

This paper introduces a Bayesian approach using Linear Gaussian State Space Models to improve fine-grained network traffic predictions by leveraging coarse-grained historical data, resulting in significantly more accurate forecasts.

Contribution

The paper presents a novel method linking coarse and fine-grained traffic data within a Bayesian state space model for enhanced prediction accuracy.

Findings

Up to 3.7 times reduction in forecast error using coarse data

Effective integration of coarse and fine data in a Bayesian framework

Improved traffic prediction performance demonstrated through experiments

Abstract

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.