SPSS Forecasting | Forecasting, Time-Series, Data, ARIMA Models

Procedures and Statistics for Analyzing Time-Series Data

Using SPSS Forecasting with SPSS Base gives you a selection of statistical techniques for analyzing time-series data and developing reliable forecasts.

Techniques tailored to time-series analysis

SPSS 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 SPSS 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

Buy Now