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.

翻译：这项工作为获得动态系统的概率间隔预测提供了新方法。该方法利用存储的过去系统测量来估计系统的未来演变情况。拟议方法依靠使用不同功能来估计产出的有条件概率密度函数。引入了以两个参数参数参数参数参数参数的实验概率密度函数组合。显示拟议组包含作为特定案例的多变正常概率密度函数。我们显示,拟议方法构成传统估算方法的概括化。使用交叉校准办法调整方法所依赖的两个参数。为了证明所用方法的有效性,提供了一些数字例子和比较。