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Time series prediction using belief network models. (English) Zbl 0827.62091

Summary: 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 generalization 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 analyse the multivariate time series of chest volume, heart rate and oxygen saturation data.

MSC:

62M20 Inference from stochastic processes and prediction
62P10 Applications of statistics to biology and medical sciences; meta analysis
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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