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| Title | On the Hyperparameter Estimation of Time Varying Poisson Model for Bayesian WWW Traffic Forecasting (in Japanese) |
|---|---|
| Authors | Daiki Koizumi 、Toshiyasu Matsushima 、Shigeichi Hirasawa |
| Released Year | 2009 |
| Format | International Conference |
| Category | Others |
| Jounal Name | 2nd International Workshop of the European Research Consortium on Infomatics and Mathematics (ERCIM) Working Group on Computing & Statistics |
| Jounal Page | p.22, Limassol, Cyprus |
| Published Year | 2009 |
| Published Month | 10 |
| Abstract （English） |
The aim is to contribute to traffic forecasting problems using a Bayesian approach for the time varying Poisson model. This model can be regarded as an application of the Simple Power Steady Model(S.P.S.M.). In this model, time variation of the parameter is formulated by a transformation function and its degree is caught by a real valued hyperparameter k (0<k<1). However, in S.P.S.M. it has not yet been proposed any definite parameter transformation function nor methods for estimation of the hyperparameter k. These two points are considered. Especially for the latter point, it has been empirically observed that World Wide Web (WWW) traffic forecasting performance strongly depends on the accuracy of the estimate of the hyperparameter k. Here at least takes two approaches for estimating the hyperparameter k, i.e. the maximum likelihood and the quasi Bayes estimations, and their effect on the traffic forecasting are discussed. According to the obtained results, the quasi Bayesian estimate gives satisfactory traffic forecasting under sufficiently large number of subintervals for integration with relatively low complexity. |
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