Method for forecasting health status of distributed networks by artificial neural networks

The present invention relates to a method for forecasting health status of a distributed network by an artificial neural network comprising the phase of identifying one or more sites, one or more assets of the sides and the links between the identified assets in said distributed network, comprising the phase of evaluating the actual health status of each of the identified assets, the phase of evaluating the actual health status of each of said identified sites and the phase of forecasting, by the artificial neural network, the subsequent health status of each of the identified sites according to a forecasting function based on a set of values comprising the actual asset health status rank, the actual asset infection risk, the actual asset infection factor, the actual site health status rank and the actual site infection risk.