Time-series prediction using belief network models

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Citation: Paul Dagum and Adam Galper. (1995) Time-series prediction using belief network models. In KSL-95-74, 1995.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Paul Dagum and Adam Galper
title Time-series prediction using belief network models
number KSL-95-74
institution Knowledge Systems, AI Laboratory
year 1995
Bibtex more
note Updated October 1995.
Access Paper
abstract We address the problem of generating normative forecasts efficiently from a Bayesian belief network. Forecasts are predictions of future values of domain variables conditioned on current and past values of domain variables. To address the forecasting problem, we have developed a probability forecasting methodology, Dynamic Network Models (DNMs), through a synthesis of belief-network models and classical time-series models. The DNM methodology is based on the integration of fundamental methods of Bayesian time-series analysis with recent additive generalizations of belief-network representation and inference techniques.We apply DNMs to the problem of forecasting episodes of apnea, that is, regular intervals of breathing cessation in patients afflicted with sleep apnea. We compare the one-step-ahead forecasts of chest volume, an indicator of apnea, made by autoregressive models, belief networks, and DNMs. We also construct a DNM to analyze the multivariate time series of chest volume, heart rate and oxygen saturation data.

KSL Technical Report ID: KSL-95-74
Facts about Time-series prediction using belief network modelsRDF feed
Abstract We address the problem of generating norma … We address the problem of generating normative forecasts efficiently from a Bayesian belief network. Forecasts are predictions of future values of domain variables conditioned on current and past values of domain variables. To address the forecasting problem, we have developed a probability forecasting methodology, Dynamic Network Models (DNMs), through a synthesis of belief-network models and classical time-series models. The DNM methodology is based on the integration of fundamental methods of Bayesian time-series analysis with recent additive generalizations of belief-network representation and inference techniques.We apply DNMs to the problem of forecasting episodes of apnea, that is, regular intervals of breathing cessation in patients afflicted with sleep apnea. We compare the one-step-ahead forecasts of chest volume, an indicator of apnea, made by autoregressive models, belief networks, and DNMs. We also construct a DNM to analyze the multivariate time series of chest volume, heart rate and oxygen saturation data. me, heart rate and oxygen saturation data.
Author Paul Dagum and Adam Galper  +
Bibtype techreport  +
Has author Paul Dagum and Adam Galper  +
Has identifier KSL-95-74  +
Has publishing details 1995  +
Has title Time-series prediction using belief network models  +
Has where published KSL-95-74  +
Has year 1995  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-95-74  +
Note Updated October 1995.
Number KSL-95-74  +
Process note YES  +
Title Time-series prediction using belief network models  +
Year 1995  +
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