  This work presents a new methodology to obtain probabilistic interval
predictions of a dynamical system. The proposed strategy uses stored past
system measurements to estimate the future evolution of the system. The 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 scalars, is introduced. It is
shown that the proposed family encompasses the multivariable normal probability
density function as a particular case. We show that the presented approach
constitutes a generalization of classical estimation methods. A validation
scheme is used to tune the two parameters on which the methodology relies. In
order to prove the effectiveness of the presented methodology, some numerical
examples and comparisons are provided.
