ABSTRACT Time series analysis is based on the continuous regularity of the development of objective things to predict the next value depending on observed values. Based on time series analysis, we present autoregressive moving average models to predict the next future value for an uncertain time series. In this paper, imprecise observations and disturbance terms are regarded as uncertain variables and assume that the latter are satisfied uncertain normal distribution. The prediction models of uncertain time series are established combining the knowledge of autoregressive model and uncertainty theory. Therefore, the interval range of the next future value is predicted based on the reliability constraint. As an illustration to compare with the numerical examples of the existing prediction method, the innovations and effectiveness of the work are further demonstrated by the computational results.

中文翻译：

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不确定时间序列自回归移动平均模型的预测方法

摘要 时间序列分析是根据客观事物发展的连续规律，根据观测值预测下一个值。基于时间序列分析，我们提出了自回归移动平均模型来预测不确定时间序列的下一个未来值。在本文中，不精确的观测和扰动项被视为不确定变量，并假设后者满足不确定正态分布。结合自回归模型和不确定性理论的知识，建立了不确定时间序列的预测模型。因此，基于可靠性约束预测下一个未来值的区间范围。作为与现有预测方法的数值例子进行比较的说明，