PASW Forecasting
Build Expert Time-Series Forecasts—in a Flash
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.
Reliable forecasts can have a major impact on your organization's ability to develop and implement successful strategies. With PASW Forecasting
(formerly called SPSS Forecasting), you have what you need to predict trends and develop forecasts quickly and easily.
Unlike spreadsheet programs, PASW 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, PASW 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
If you're new to building models from time-series data, PASW Forcasting 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 an experienced PASW Statistics user PASW Forecasting allows you to:
- Control every parameter when building your data model
- Or use PASW Trends' Expert Modeler recommendations as a starting point or to check your work
Key features available in PASW Forecasting 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
Procedures and Statistics for Analyzing Time-Series Data
Using PASW Forecasting with PASW Statistics Base gives you a selection of statistical techniques for analyzing time-series data and developing reliable forecasts.
Techniques Tailored to Time-Series Analysis
PASW Statistics has the procedures you need get the most benefit from your time-series analysis. It generates statistics and normal probability plots, so
that you can easily judge model fit. You can even limit output so that you see only the worst-fitting models—those that require further examination. Automatically generated high-resolution charts enhance your output.
Procedures available in PASW Forecasting include:
- 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
Back to PASW Statistics Base page.
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