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On the Estimation of Missing Data in Incomplete Databases: Autoregressive Bayesian Networks

Authors:
Pablo H. Ibargüengoytia
Uriel A. García
Javier Herrera Vega
Pablo Hernández Leal
Eduardo Morales
L. Enrique Sucar
Felipe Orihuela Espina

Keywords: dynamic probabilistic graphical models; incomplete data series; value estimation; knowledge discovery; autoregressive models

Abstract:
Missing data can be estimated by means of interpolation, time series modelling, or exploiting statistically dependent information. The limits of when one approach is preferable to the alternatives have not been explored, but are likely to be a compromise between a signal autoregressive information, availability of future observations, stationary behaviour and the strength of the dependence with concomitant information. This paper takes a first step towards clarifying dataset characteristics delimiting the realm of application for each technique. In addition, this paper introduces autoregressive Bayesian networks (AR-BN), a variant of Dynamic Bayesian Networks for completing databases which exploits latent variable relations while still benefitting from autoregressive information of the variable being filled. Using AR-BN, new estimated values are calculated using inference in the dynamic model. Our results unveil how the interplay between the variable autoregressive information and the variable relationship to others in the dataset is critical to selecting the optimal data estimation technique. AR-BN appears as a good candidate ensuring a consistent performance across scenarios, datasets and error metrics

Pages: 111 to 116

Copyright: Copyright (c) IARIA, 2013

Publication date: January 27, 2013

Published in: conference

ISSN: 2308-4243

ISBN: 978-1-61208-246-2

Location: Seville, Spain

Dates: from January 27, 2013 to February 1, 2013