CARIMA Procedure

The CARIMA procedure analyzes and forecasts equally spaced univariate time series data, by using the autoregressive integrated moving-average (ARIMA) or autoregressive moving-average (ARMA) model.

An ARIMA model predicts a value in a response time series as a linear combination of its own past values and past errors (also called shocks or innovations).

The ARIMA approach was first popularized by Box and Jenkins (1976), and ARIMA models are often referred to as Box-Jenkins models.

The CARIMA procedure provides a comprehensive set of tools for parameter estimation and for forecasting univariate time series. It also offers a high degree of flexibility in the types of ARIMA models that you can analyze; it supports seasonal, subset, and factored ARIMA models. For each fitted model, PROC CARIMA prints parameter estimates along with standard errors and p-values, and it prints multistep-ahead variable forecasts along with confidence intervals.

Before you use PROC CARIMA, you should exercise care and judgment and be familiar with Box-Jenkins methods.

The ARIMA class of time series models is complex and powerful, and some degree of expertise is needed to use them correctly.

PROC CARIMA requires SAS Cloud Analytic Services (CAS) in order to run, and it does the following: