ARIMA (Box-Jenkins) and ARIMAX (Box-Tiao) Modeling and Forecasting
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, include 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
- 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
- exact maximum likelihood
- conditional least squares
- exact nonlinear unconditional least squares
- 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
- a variety of model diagnostic statistics including
- 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
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
