SPSS Forecasting 17.0

Build Expert Time-Series Forecasts—in a Flash
Reliable forecasts can have a major impact on your organization’s ability to develop and implement successful strategies. With SPSS Forecasting (formerly called SPSS Trends), you have what you need to predict trends and develop forecasts quickly and easily.

Unlike spreadsheet programs, SPSS Forecasting has the advanced statistical techniques you need in order to work with time-series data. But you don’t need to be an expert statistician to use it.

Regardless of your level of experience, you can analyze historical data and predict trends faster, and deliver information in ways that your organization’s decision makers can understand and use.

Thanks to its Expert Modeler feature, SPSS Forecasting:

• Automatically determines the best-fitting ARIMA or exponential smoothing model to analyze your historic data
• Enables you to model hundreds of different time series at once, rather than having to run the procedure for one variable at a time

• Generating reliable models, even if you’re not sure how to choose exponential smoothing parameters or ARIMA orders, or how to achieve stationarity
• Automatically testing your data for seasonality, intermittency, and missing values, and selecting appropriate models
• Detecting outliers and preventing them from influencing parameter estimates
• Generating graphs showing confidence intervals and the model’s goodness of fit

• Control every parameter when building your data model
• Or use SPSS Trends’ Expert Modeler recommendations as a starting point or to check your work

• Save models to a central file so that forecasts can be updated when data changes, without having to re-set parameters or re-estimate the model
• Write scripts so that models can be updated with new data automatically

• TSMODEL: Use the Expert Modeler to model a set of time-series variables, using either ARIMA or exponential smoothing techniques
• TSAPPLY: Apply saved models to new or updated data
• SEASON: Estimate multiplicative or additive seasonal factors for periodic time series
• SPECTRA: Decompose a time series into its harmonic components, which are sets of regular periodic functions at different wavelengths or periods