• DocumentCode
    2313849
  • Title

    Dynamic Bayesian approach to forecasting

  • Author

    Tang, Adelina

  • Author_Institution
    Sch. of Comput. Technol., Sunway Univ. Coll., Petaling Jaya, Malaysia
  • Volume
    8
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    3933
  • Lastpage
    3937
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5584772
  • Filename
    5584772
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2313849