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 Trends, an add-on module to SPSS Base, you have what you need to predict trends and develop forecasts quickly and easily.

Unlike spreadsheet programs, SPSS Trends 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 Trends:

• 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

If you’re new to building models from time-series data, SPSS Trends helps you by:

• 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

If you’re experienced at forecasting, SPSS Trends allows you to:

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

Key features available in SPSS Trends enable you to:

• 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

This data chart illustrates men's clothing sales, raw and seasonally differenced over a 10-year period. Using seasonal difference helps to clarify the relationships within your data.