Forecasting time series from clustering by a memetic differential fuzzy approach: An application to crime prediction

This paper presents a method to forecast spatiotemporal patterns of criminal activity, through a novel time series approach from fuzzy clustering, in the city of San Francisco, USA. The developed analysis comprises from the cluster quantity selection to the forecast. A memetic algorithm is proposed in order to execute the series forecast, as well as, a problem-oriented fitness function. Results show that series approach of fuzzy clustering for criminal patterns is a feasible method to produce a forecast of criminal patterns.

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