Autoregressive, Integrative, Moving Average (ARIMA) Models

ARIMA is an acronym for autoregressive, integrative, moving average models. It is an approach to time-series analysis sometimes referred to as the [Page 77]Box-Jenkins approach. The three terms that compose the name are three different forms of serial dependency often found in time-series data. Because time-series data are repeated measures on the same case sequenced in time, each observation is not independent of the other observations in the sequence (i.e., serial dependency) resulting in correlated errors. This means that the data violate the assumptions of most standard traditional statistical tests like regression and analysis of variance (ANOVA). ARIMA models are stochastic in that they assume that the serial dependency is greatest among observations that are temporally proximate (e.g., values adjacent in a sequence), but that ...