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Proceedings Paper

Problem signatures from enhanced vector autoregressive modeling
Author(s): Bruno R. Andriamanalimanana; Saumen S. Sengupta
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Paper Abstract

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.

Paper Details

Date Published: 19 September 2001
PDF: 10 pages
Proc. SPIE 4367, Enabling Technology for Simulation Science V, (19 September 2001); doi: 10.1117/12.440026
Show Author Affiliations
Bruno R. Andriamanalimanana, SUNY Institute of Technology at Utica/Rome and Profesy International Inc. (United States)
Saumen S. Sengupta, SUNY Institute of Technology at Utica/Rome and Profesy International Inc. (United States)


Published in SPIE Proceedings Vol. 4367:
Enabling Technology for Simulation Science V
Alex F. Sisti; Dawn A. Trevisani, Editor(s)

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