Probabilistic interval predictor based on dissimilarity functions
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This work presents a new method to obtain probabilistic interval predictions of a dynamical system. The method uses stored past system measurements to estimate the future evolution of the system. The proposed method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two parameters, is introduced. It is shown that the the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the proposed method constitutes a generalization of classical estimation methods. A cross-validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the methodology presented, some numerical examples and comparisons are provided.
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