Problem signatures from enhanced vector autoregressive modeling

The work reported in this paper concerns the enhancement of mutivariate autoregressive (AR) models with geometric shape analysis data and stochastic causal relations. The study aims at producing numerical signatures characterizing operating problems, from multivariate time series of data collected in an application and operating environment domain. Since the information content of an AR model does not appear sufficient to characterize observed vector values fully, both geometric and stochastic modeling techniques are applied to refine causal inferences further. The specific application domain used for this study is real-time network traffic monitoring. However, other domains utilizing vector models might benefit as well. A partial Java implementation is being used for experimentation.