Bayesian Forecasting of WWW Traffic on the Time Varying Poisson Model

TL;DR

This paper introduces a Bayesian forecasting method for WWW traffic using a time-varying Poisson model, emphasizing low computational complexity and strong empirical performance for network planning.

Contribution

It presents a novel Bayesian approach for WWW traffic forecasting based on a time-varying Poisson model, with simple calculations and validated effectiveness.

Findings

Forecasting values are obtained by simple arithmetic calculations.

The model accurately reflects real WWW traffic.

The approach is effective both theoretically and empirically.

Abstract

Traffic forecasting from past observed traffic data with small calculation complexity is one of important problems for planning of servers and networks. Focusing on World Wide Web (WWW) traffic as fundamental investigation, this paper would deal with Bayesian forecasting of network traffic on the time varying Poisson model from a viewpoint from statistical decision theory. Under this model, we would show that the estimated forecasting value is obtained by simple arithmetic calculation and expresses real WWW traffic well from both theoretical and empirical points of view.