Forecasting Sleep Apnea with Dynamic Network Models

03/06/2013
by   Paul Dagum, et al.
0

Dynamic network models (DNMs) are belief networks for temporal reasoning. The DNM methodology combines techniques from time series analysis and probabilistic reasoning to provide (1) a knowledge representation that integrates noncontemporaneous and contemporaneous dependencies and (2) methods for iteratively refining these dependencies in response to the effects of exogenous influences. We use belief-network inference algorithms to perform forecasting, control, and discrete event simulation on DNMs. The belief network formulation allows us to move beyond the traditional assumptions of linearity in the relationships among time-dependent variables and of normality in their probability distributions. We demonstrate the DNM methodology on an important forecasting problem in medicine. We conclude with a discussion of how the methodology addresses several limitations found in traditional time series analyses.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/13/2013

Dynamic Network Models for Forecasting

We have developed a probabilistic forecasting methodology through a synt...
research
03/06/2013

Additive Belief-Network Models

The inherent intractability of probabilistic inference has hindered the ...
research
03/20/2013

ARCO1: An Application of Belief Networks to the Oil Market

Belief networks are a new, potentially important, class of knowledge-bas...
research
12/03/2012

Nonparametric risk bounds for time-series forecasting

We derive generalization error bounds for traditional time-series foreca...
research
03/13/2013

A computational scheme for Reasoning in Dynamic Probabilistic Networks

A computational scheme for reasoning about dynamic systems using (causal...
research
11/26/2020

Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches

In this paper, we propose two nonparametric methods used in the forecast...
research
09/20/2021

Modeling Regime Shifts in Multiple Time Series

We investigate the problem of discovering and modeling regime shifts in ...

Please sign up or login with your details

Forgot password? Click here to reset