Knowledge Discovery and Self-Organizing State Space Model

Tomoyuki HIGUCHI  Genshiro KITAGAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E83-D   No.1   pp.36-43
Publication Date: 2000/01/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: INVITED PAPER (Special Issue on Surveys on Discovery Science)
non-Gaussian non-linear time series model,  generalized state space model,  self-organizing state space model,  hierarchical structure model,  Bayesian model,  

Full Text: PDF(473.2KB)>>
Buy this Article

A hierarchical structure of the statistical models involving the parametric, state space, generalized state space, and self-organizing state space models is explained. It is shown that by considering higher level modeling, it is possible to develop models quite freely and then to extract essential information from data which has been difficult to obtain due to the use of restricted models. It is also shown that by rising the level of the model, the model selection procedure which has been realized with human expertise can be performed automatically and thus the automatic processing of huge time series data becomes realistic. In other words, the hierarchical statistical modeling facilitates both automatic processing of massive time series data and a new method for knowledge discovery.