IBM SPSS Forecasting

IBM SPSS Forecasting offers a number of capabilities that enable both novice and experienced users to quickly develop reliable forecasts using time-series data. It is a fully integrated module of IBM SPSS Statistics, giving you all of IBM SPSS Statistics’ capabilities plus features specifically designed to support forecasting.

New to Building Models from Time-series Data?

IBM SPSS Forecasting 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

You’re an Experienced IBM SPSS Statistics User?

IBM SPSS Forecasting allows you to:

• Control every parameter when building your data model
• Use IBM SPSS Forecasting Expert Modeler recommendations as a starting point or to check your work

Procedures and Statistics for Analyzing Time-series Data

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

Techniques Tailored to Time-series Analysis

IBM SPSS Statistics has the procedures you need to realize 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 to see only the worst-fitting models -- those that require further examination. Automatically generated high-resolution charts enhance your output.

Procedures available in IBM 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