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Overview of the Time Series Forecasting System


The Time Series Forecasting system forecasts future values of time series variables by extrapolating trends and patterns in the past values of the series or by extrapolating the effect of other variables on the series. The system provides convenient point-and-click windows to control the time series analysis and forecasting tools of SAS/ETS software.

You can use the system in a fully automatic mode, or you can use the system’s diagnostic features and time series modeling tools interactively to develop forecasting models customized to best predict your time series. The system provides both graphical and statistical features to help you choose the best forecasting method for each series.

The following is a brief summary of the features of the Time Series Forecasting system. You can use the system in the following ways:

  • use a wide variety of forecasting methods, including several kinds of exponential smoothing models, Winters method, and ARIMA (Box-Jenkins) models. You can also produce forecasts by combining the forecasts from several models.

  • use predictor variables in forecasting models. Forecasting models can include time trend curves, regressors, intervention effects (dummy variables), adjustments you specify, and dynamic regression (transfer function) models.

  • view plots of the data, predicted versus actual values, prediction errors, and forecasts with confidence limits, as well as autocorrelations and results of white noise and stationarity tests. Any of these plots can be zoomed and can represent raw or transformed series.

  • use hold-out samples to select the best forecasting method

  • compare goodness-of-fit measures for any two forecasting models side by side or list all models sorted by a particular fit statistic

  • view the predictions and errors for each model in a spreadsheet or compare the fit of any two models in a spreadsheet

  • examine the fitted parameters of each forecasting model and their statistical significance

  • control the automatic model selection process: the set of forecasting models considered, the goodness-of-fit measure used to select the best model, and the time period used to fit and evaluate models

  • customize the system by adding forecasting models for the automatic model selection process and for point-and-click manual selection

  • save your work in a project catalog

  • print an audit trail of the forecasting process

  • show source statements for PROC ARIMA code

  • save and print system output including spreadsheets and graphs

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