Optimal sampling strategies for tree-basedtime seriesIn this paper we consider binary multiscale tree models of timeR. Riedi

Rice UniversityAbstract

series. In this setting, the overall average of a time series is

represented by the tree root, the average over the first half of the

series by its left child node, and so on. The time series itself

is represented by the leaf nodes. We address the problem of

choosing a limited number of leaf nodes to provide the optimal

linear least-square estimator of the tree root. Such problems

arise in network traffic inference where the goal lies in

estimating the average traffic arrival rate based on a limited

number of traffic samples. The solution depends crucially on the

correlation structure in the time series.Joint work with V. Ribeiro, R. Baraniuk