Nonparametric Modelling of Cyclo-Stationary Markovian Processes Part II: prediction and dimension reduction

摘要

This paper deals with spatio-temporal conditional prediction of sea state parameters given nearby observations of the same parameters or given the observations of other sea state parameters at the same geographic point. An algorithm referred as Non Parametric Viterbi(NPV) AND BASED ON Hidden Markov Chain theory is proposed. It is shown that this algorithm can be used, for instance, to predict missing values in sea state data networks such as bouy networks or to predict a sea state process given part of the multivariate observed vector. The reduction of the dimension of the representation state space in which the process is described is also of practical use since this allows jointly to reduce the size of the learning data set and to maintain the algorithmic complexity at a tractable level.