[[統計学]]および[[経済学]]、とりわけ[[時系列解析]]の分野において、’’’自己回帰和分移動平均モデル’’’ （じこかいきわぶんいどうへいきんモデル） {{lang-en-short|Autoregressive integrated moving average model}}、’’’ARIMAモデル’’’ [[Mathematical model|model]]）は、[[自己回帰移動平均モデル]] （ARMA）を一般化したモデルです。これらのモデルは、
In [[statistics]] and [[econometrics]], and in particular in [[time series analysis]], an ’’’autoregressive integrated moving average (ARIMA)‘’’ [[Mathematical model|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 [[Stationary process|non-stationarity]], where an initial differencing step (corresponding to the “integrated” part of the model) can be applied to remove the non-stationarity.
The model is generally referred to as an ARIMA model where [[parameter]]s ’’p’’, ’’d’’, and ’’q’’ are non-negative integers that refer t