| Summary: | The paper present a solution for the economic activity evolution diagnostic and prediction by means of a set of indicators. Starting from the indicators set, there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indicators set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostic and prediction the following tools are used: pattern recognition and multilayer perceptron implemented in the REFORME software written by the author and the results of the experiment obtained with this software for macroeconomic diagnostic and prediction during the years 2005-2012 for diagnostic and 2013-2014 for prediction. <br /><br /><strong>Keywords: </strong>pattern recognition, neural network, multilayer perceptron, indicators, diagnostic, prediction.
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