State-parameter dependency estimation of stochastic time series using data transformation and parameterization by support vector regression

This position paper is about the identification of the dependency among parameters and states in regression models of stochastic time series. Conventional recursive algorithms for parameter estimation do not provide good results in models with state-dependent parameters (SDP) because these may have highly non-linear behavior. To detect this dependence using conventional algorithms, we are studying some data transformations that we implement in this paper. Non-parametric relationships among parameters and states are obtained and parameterized using support vector regression. This way we look for a final non-linear structure to solve the SDP identification problem.