IEICE TRANS INF SYST VOL E A NO JANUARY PAPER Surveys on Discovery Science Knowledge Discovery and Self Organizing State Space Model

A hierarchical structure of the statistical mod els 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 devel op models quite freely and then to extract essential information from data which has been di cult 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 real ized 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 fa cilitates both automatic processing of massive time series data and a new method for knowledge discovery key words non Gaussian non linear time series model gener alized state space model self organizing state space model hier archical structure model Bayesian Model