Spatiotemporal Forecasting of Housing Prices by Use of Genetic Programming
نویسنده
چکیده
Complexity of space-time analysis remains a major problem faced by forecasters. Theoretical issues and forecast inaccuracy emanate from specification error, aggregation error, measurement error, and perhaps model complexity. Because such problems are mainly statistical in nature, employing techniques not based on statistical methods is tested here. Two computational techniques (genetic programming and neural networks) are employed to demonstrate their potential. Their forecasts can help deliver sequences of maps of the same geographic region depicting future temporal changes.
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تاریخ انتشار 2006