Time Series Modeling and Forecasting
The Forecasting add-on module provides the following procedures for accomplishing the tasks of creating models and producing forecasts:
- The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and produces forecasts. The procedure includes an Expert Modeler that attempts to automatically identify and estimate the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus eliminating the need to identify an appropriate model through trial and error. Alternatively, you can specify a custom ARIMA or exponential smoothing model.
- The Apply Time Series Models procedure applies existing time series models--created by the Time Series Modeler--to the active dataset. You can use this procedure to obtain forecasts for series for which new or revised data are available, without rebuilding your models.
- The Temporal Causal Models procedure builds autoregressive time series models for each target and automatically determines the best inputs that have a causal relationship with the target. The procedure produces interactive output that you can use to explore the causal relationships. The procedure can also generate forecasts, detect outliers, and determine the series that most likely causes an outlier.