Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (17 chapters)
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Automating time series forecasts


The Expert Modeler functionality in Modeler greatly simplifies time series forecasting. The Time Series node will automatically determine which model type is most appropriate for your data: ARIMA, exponential smoothing, seasonal model, and so on. However, in practice, a time series model nugget can only generate forecast models for a single time series. It is possible to generate multiple time series forecasts using the Time Series node but it is largely impractical. First, you must pivot the data such that each series is a column. Second, defining input variable roles can become convoluted due to each field having only a single role (for example, a field cannot be an input for one series but none for another input). Finally, you must reverse-pivot the forecast data back to the original format to make use of it. This reverse pivot requires you to have a fixed set of input names to pivot. With all of these limitations, the Time Series node does not scale to...