Visualizing And Exploring Multiple Time Series

Aim of this paper is to present a way to explore time series by combining some fundamental results of stochastic processes theory with graphical and reduction features of factorial methods. A multiple time series visualization and identification strategy is provided by defining a common structural subspace where different regions related to particular ARMA processes are represented. This subspace becomes the reference map for the exploration of multiple time series. In order to provide a unique identification of the ARMA model, a complementary tool represented by a classification tree is proposed.