The concept of fuzzy random variable combines two sources of uncertainty: randomness and fuzziness, whereas the model of ordered fuzzy numbers provides a representation of inaccurate quantitative data, and is an alternative to the standard fuzzy numbers model proposed by Zadeh. This paper develops the model of ordered fuzzy numbers by defining the concept of fuzzy random variables for these numbers, called further ordered fuzzy random variables. Thanks to the well-defined arithmetic of ordered fuzzy numbers (existence of neutral and opposite elements) and the introduced ordered fuzzy random variables; it becomes possible to construct fully fuzzy stochastic time series models such as e.g., the autoregressive model or the GARCH model in the form of classical equations, which can be estimated using the least-squares or the maximum likelihood method. Furthermore, the concept of normality of ordered fuzzy random variables and the method to generate pseudo-random ordered fuzzy variables with normal distribution are introduced.

模糊随机变量的概念结合了不确定性的两个来源：随机性和模糊性，而有序模糊数模型提供了不准确的定量数据的表示形式，并且是Zadeh提出的标准模糊数模型的替代方法。本文通过为这些数字定义模糊随机变量的概念（称为进一步有序模糊随机变量）来开发有序模糊数字模型。得益于明确定义的有序模糊数算法（存在中性和相反元素）和引入的有序模糊随机变量；以经典方程式的形式构建完全模糊的随机时间序列模型（例如自回归模型或GARCH模型）成为可能，可以使用最小二乘法或最大似然法进行估算。