1. A method for predicting at least one system event comprising: determining, by at least one sensor, values s j of at least one primary parameter S j within at least one time window having the length L, determining, by at least one data processing device connected to the at least one sensor, a frequency τ of a trend for at least a portion of the at least one primary parameter S j for at least a part of the time window, and signaling, by the at least one data processing device, an occurrence of a system event based on the frequency τ of a trend or initiating at least one system event-related measure to address the system event, wherein the frequency τ of a trend for the at least one time window of the length L, L min ≦L≦L max , and for a time of prediction H, H min ≦H≦H max is determined, wherein the window length L and the time of prediction H are different length time periods occurring before the system event, wherein the at least one time window is comprised of a plurality of time windows, groups of primary parameters S j are formed, and an average {circumflex over (T)} i ± for at least one of frequency τ and frequency τ i for the time windows are found for at least one group q of primary parameters, wherein features T k ± formed from the primary parameters are combined in the at least one group q according to T k ± = 1 n i q ⁢ ∑ j = 1 n i q ⁢ ⁢ β j ⁢ T ^ j ± ⁡ ( S j ) ∑ j = 1 n i q ⁢ ⁢ β j , k = 1 , … ⁢ , N q , wherein N q is the number of groups, each group q combining a number of n i q measured variables (where i=1,. .. , N q ), and the group q comprising only one {circumflex over (T)} i ± , and the measured variables determined, weighted by relevance, using weighting factors β j , wherein T k + and T k − , which belong to a same group, are included in a classification method as combined variable T k ± , and wherein the classification method provides an indicator to suggest the occurrence of the system event.