An approach to predict from a data stream of real estate sales transactions based on ensembles of genetic fuzzy systems was presented. The proposed method relies on incremental expanding an ensemble by models built over successive chunks of a data stream. The output of aged component models produced for current data is updated according to a trend function reflecting the changes of premises prices since the moment of individual model generation or the beginning of the data stream. The impact of different trend functions on the accuracy of single and ensemble fuzzy models was investigated in the paper. Intensive experiments were conducted to evaluate the proposed method using real-world data taken from a dynamically changing real estate market. The statistical analysis of experimental output was made employing the nonparametric methodology designed especially for multiple comparisons including Friedman tests followed by Nemenyi's, Holm's, Shaffer's, and Bergmann-Hommel's post-hoc procedures. The results proved the usefulness of ensemble approach incorporating the correction of individual component model output.

genetic fuzzy systems, data stream, sliding windows, ensembles, predictive models, trend functions, Property valuation