Article Title

A Three-Step Combined Genetic Programming and Neural Networks Method of Forecasting the S&P/Case-Shiller Home Price Index

Department/School

School of Business

Abstract

Forecasts of the San Diego and San Francisco S&P/Case-Shiller Home Price Indices through December 2012 are obtained using a multi-agent system that utilizes January, 2002–June, 2011 data. Agents employ genetic programming (GP) and neural networks (NN) in a three-stage process to produce fits and forecasts. First, GP and NN compete to provide independent predictions. In the second stage, they cooperate by fitting the first-stage competitor's residuals. Outputs from the first two stages then become inputs to produce two final GP and NN outputs. The NN output from the third stage using the combined method produces improved forecasts over the 3-stage GP method as well as those produced by either method alone. The proposed methodology serves as an example of how combining more than one estimation/forecasting technique may lead to more accurate forecasts.

Document Type

Article

Publication Title

International Journal of Computational Intelligence and Applications

Publication Date

3-14-2013

Volume

12

Issue

1

Digital Object Identifier (DOI)

10.1142/S1469026813500016

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