The ARIMA procedure provides the identification, parameter estimation, and forecasting of autoregressive integrated moving-average (Box-Jenkins) models, seasonal ARIMA models, transfer function models, and intervention models. The ARIMA procedure includes the following features:
complete ARIMA (Box-Jenkins) modeling with no limits on the order of autoregressive or moving-average processes
model identification diagnostics including the following:
autocorrelation function
partial autocorrelation function
inverse autocorrelation function
cross-correlation function
extended sample autocorrelation function
minimum information criterion for model identification
squared canonical correlations
stationarity tests
outlier detection
intervention analysis
regression with ARMA errors
transfer function modeling with fully general rational transfer functions
seasonal ARIMA models
ARIMA model-based interpolation of missing values
several parameter estimation methods including the following:
exact maximum likelihood
conditional least squares
exact nonlinear unconditional least squares (ELS or ULS)
prewhitening transformations
forecasts and confidence limits for all models
forecasting tied to parameter estimation methods: finite memory forecasts for models estimated by maximum likelihood or exact nonlinear least squares methods and infinite memory forecasts for models estimated by conditional least squares
diagnostic statistics to help judge the adequacy of the model including the following:
Akaike’s information criterion (AIC)
Schwarz’s Bayesian criterion (SBC or BIC)
Box-Ljung chi-square test statistics for white-noise residuals
autocorrelation function of residuals
partial autocorrelation function of residuals
inverse autocorrelation function of residuals
automatic outlier detection