The UCM procedure analyzes and forecasts equally spaced univariate time series data by using an unobserved components model (UCM). The UCMs are also called structural models in the time series literature. A UCM decomposes the response series into components such as trend, seasonals, cycles, and the regression effects due to predictor series. The components in the model are supposed to capture the salient features of the series that are useful in explaining and predicting its behavior.

You can use the UCM procedure to fit a wide range of UCMs that can incorporate complex trend, seasonal, and cyclical patterns and can include multiple predictors. The UCM procedure provides a variety of diagnostic tools to assess the fitted model and to suggest the possible extensions or modifications. The components in the UCM provide a succinct description of the underlying mechanism governing the series. You can print, save, or plot the estimates of these component series. Along with the standard forecast and residual plots, the study of these component plots is an essential part of time series analysis using the UCMs. Once a suitable UCM is found for the series under consideration, it can be used for a variety of purposes. For example, it can be used for the following:

- forecasting the values of the response series and the component series in the model
- obtaining a model-based seasonal decomposition of the series
- obtaining a version with noise removed and interpolating the missing values of the response series in the historical period
- obtaining the full sample of smoothed estimates of the component series in the model

For further details, see the *SAS/ETS ^{®} User's Guide*

- Example 41.1: The Airline Series Revisited
- Example 41.2: Variable Star Data
- Example 41.3: Modeling Long Seasonal Patterns
- Example 41.4: Modeling Time-Varying Regression Effects
- Example 41.5: Trend Removal Using the Hodrick-Prescott Filter
- Example 41.6: Using Splines to Incorporate Nonlinear Effects
- Example 41.7: Detection of Level Shift
- Example 41.8: ARIMA Modeling