Book Image

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
Book Image

IBM SPSS Modeler Cookbook

By: Keith McCormick, Abbott

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 (11 chapters)
10
Index

Building iterative Neural Network forecasts


Artificial Neural Networks (ANN) models provide a robust method of generating forecasts. ANN can be built using nearly any input types including categorical, flag, and continuous inputs. ANN models are relatively insensitive to outliers and are capable of capturing subtle interactions between input variables. All of these benefits have made ANN models increasingly popular for many applications such as forecasting product sales, energy demand, spot market prices, and so on.

Even though ANN forecast models are generally superior to traditional forecasting techniques such as ARIMA, they do have a few drawbacks. The first drawback of ANN forecast models is that they are not autoregressive (as compared to ARIMA). The model builder must choose the appropriate lags for the input variables. For example, do we look at the price one day ago, one week ago, or one month ago when predicting the current price? The second drawback is that the ANN models predict...