An analysis of change trends by predicting from a data stream using neural networks

Abstract

A method to predict from a data stream of real estate sales transactions based on ensembles of artificial neural networks was proposed. The approach consists in incremental expanding an ensemble by models built over successive chunks of a data stream. The predicted prices of residential premises computed by aged component models for current data are updated according to a trend function reflecting the changes of the market. The impact of different trend functions on the accuracy of ensemble neural models was investigated in the paper. The results indicate it is necessary to make selection of correcting functions appropriate to the nature of market changes. © 2013 Springer-Verlag Berlin Heidelberg.