SPSS Trends - predict trends

SPSS Trends

Improve your forecasts using powerful time-series data analysis

Time-series analysis is the most powerful tool you have for analyzing historical information, building models and forecasting future events. Whatever data you examine - sales figures, student enrollments, crime rates - SPSS Trends, an SPSS add-on module, gives you an easy-to-use graphical interface to analyze historical information and predict future events. Use SPSS Trends to ensure the decision makers in your organization can set long-term goals - and know how to achieve them - based on your organization's past performance and knowledge of your industry. You can use SPSS Trends to:

Monitor quality standards
Manage forecasting systems performance
Run sales forecasts
Study public opinion
And more

Make your analysis easier
SPSS Trends gives you complete and flexible time-series tools with an easy-to-use graphical interface (GUI). Fine tune or adjust your analysis in just a few mouse clicks using dialog boxes that illustrate every step. Enhance your output with automatic, high-resolution charts.

SPSS Trends chart

More statistics for data analysis

Expand SPSS Base's capabilities for the data analysis stage in the analytical process. Using SPSS Trends with SPSS Base gives you a range of statistics so you can analyze time-series data and predict events. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.

Check your models with rich diagnostics
SPSS Trends generates statistics and normal probability plots to assess how models fit your data. Easily judge fit with automatically created standard errors and other statistics. You can also enhance your output using automatic, high-resolution charts. When you generate goodness-of-fit statistics, the display automatically separates the statistics for historical and validation periods so you see them side by side.

SPSS Trends has the procedures you need get the most from your time-series analysis. These procedures are:

ARIMA: produces maximum likelihood estimates for seasonal or non-seasonal time-series data
EXSMOOTH: uses exponential smoothing methods to estimate up to four parameters in 12 different models
SEASON: estimates multiplicative or additive seasonal factors for periodic time series
SPECTRA: decomposes a time series into its harmonic components, sets of regular periodic functions at different wavelengths or periods
AREG: estimates a regression model when the error from regression is correlated between one time point and the next