Spatiotemporal forecasting of home prices: A GIS application

Computational techniques may be useful in modelling and forecasting spatiotemporal data. Statistical challenges that emanate from specification error, aggregation error, measurement error, and perhaps model complexity among other problems encourage employing computational techniques. Genetic programming and neural networks are two such techniques that are robust with respect to autocorrelation, multicollinearity, and stationarity problems statistical and econometric methods encounter. These two computational techniques are employed to demonstrate their potential in producing dynamic forecasts of spatial data. Such forecasts can then help produce sequences of maps of the same geographic region depicting future temporal changes. Copyright © 2005 IFAC