ARIMA模型（英语：Autoregressive Integrated Moving Average model），差分集成移动平均自回归模型，又称集成移动平均自回归模型（移动也可称作滑动），时间串行预测分析方法之一。ARIMA（p，d，q）中，AR是"自回归"，p为自回归项数；MA为"滑动平均"，q为滑动平均项数，d为使之成为平稳串行所做的差分次数（阶数）。「差分」一词虽未出现在ARIMA的英文名称中，却是关键步骤。

ARIMA（p，d，q）模型是ARMA（p，q）模型的扩展。ARIMA（p，d，q）模型可以表示为：

In statistics and econometrics, and in particular in time series analysis, an autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series (forecasting). They are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the "integrated" part of the model) can be applied to reduce the non-stationarity.