自回归滑动平均模型（英语：Autoregressive moving average model，简称：ARMA模型）。是研究时间串行的重要方法，由自回归模型（简称AR模型）与滑动平均模型（简称MA模型）为基础“混合”构成。在市场研究中常用于长期追踪资料的研究，如：Panel研究中，用于消费行为模式变迁研究；在零售研究中，用于具有季节变动特征的销售量、市场规模的预测等。

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the auto-regression and the second for the moving average. The general ARMA model was described in the 1951 thesis of Peter Whittle, Hypothesis testing in time series analysis, and it was popularized in the 1971 book by George E. P. Box and Gwilym Jenkins.