1. A method for making predictions based on a non-parametric empirical model used in monitoring a system, the method comprising: providing a processor; receiving at the processor a query vector of multiple query sensor values for different monitored parameters for the non-parametric empirical model characterizing the system; determining, with the processor, robust distances between the query vector and each of a set of predetermined historical vectors, each historical vector having multiple historical sensor values, for the non-parametric empirical model based on an implementation of an elemental kernel function including: performing an elemental calculation between each query sensor value of the query vector and a corresponding historical sensor value of each historical vector, wherein each elemental calculation results in a single elemental contributor for each pair of corresponding query and historical sensor values, eliminating at least one, but less than all, of the elemental contributors formed from at least one comparison between the query vector and the historical vector, and depending on the values of the elemental contributors, calculating the robust distance between the query vector and the historical vector using a calculation with the remaining elemental contributors; determining weights for the monitored parameters based on the robust distances calculated using the query vector; combining the weights with the predetermined historical vectors to make predictions for the system.