Title :
Dynamic Bayesian approach to forecasting
Author_Institution :
Sch. of Comput. Technol., Sunway Univ. Coll., Petaling Jaya, Malaysia
Abstract :
Bayesian belief propagation is flexible and highly adaptable in machine learning and artificial intelligence methodologies. Coupled with a time element, the Dynamic Bayesian approach has shown promise in forecasting applications. A methodology consisting of beliefs propagated through the TAN-Pearl network and computed for every time slice is proposed to this end. Benchmark comparisons indicate encouraging results.
Keywords :
belief networks; learning (artificial intelligence); trees (mathematics); Bayesian belief propagation; TAN-Pearl network; artificial intelligence methodologies; machine learning; time slice; Additives; Artificial neural networks; Bayesian methods; Forecasting; Machine learning; Training; dynamic bayesian; forecasting; time-slice; tree augmented;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5584772
