Overview for Forecast with Best ARIMA Model

Box-Jenkin's autoregressive integrated moving average (ARIMA) models are powerful tools to fit time series data sets and to forecast future values. Even so, the identification of adequate autoregressive and moving average orders in an ARIMA model is difficult and time-consuming.

Use Forecast with Best ARIMA Model to significantly speed up the model identification process by automatically selecting the best model from a candidate set with one of the three commonly-used model-selection criteria: Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), and Bayesian Information Criterion (BIC).

For example, to plan resources efficiently, the administrators at a hospital want to use an ARIMA model to forecast the number of outpatient visits per day. Although the administrators see patterns in the time series that suggest certain orders of terms for an ARIMA model, the administrators want to quickly compare a large number of seasonal and non-seasonal ARIMA models to find a model that fits the data well. The administrators use Forecast with Best ARIMA Model to quickly evaluate a large number of models.