KSL-95-74 + redirect page
Time-series prediction using belief network models + Has identifier
Time-series prediction using belief network models + Ksl tr id
Time-series prediction using belief network models + Number
| Time-series prediction using belief network models |
Bibtype
techreport
Has publishing details
1995
Has title
Time-series prediction using belief network models
Has where published
KSL-95-74
Has year
1995
Title
Time-series prediction using belief network models
Year
1995
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.
Note
Updated October 1995.
Author
Paul Dagum and Adam Galper +
Has author
Paul Dagum and Adam Galper +
Has identifier
Time-series prediction using belief network models +
Institution
Knowledge Systems, AI Laboratory +
Ksl tr id
Time-series prediction using belief network models +
Number
Time-series prediction using belief network models +
Process note
YES +
Categories KSL Technical Report +, Publication +, Technical Report +
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