1. A processor-implemented method of generating a data model for analysis of data representative of a physical process over a period of time, the data model being based on a set of model input variables, comprising: performing a variable predictiveness determination on a population of candidate variables using a processor, the variable predictiveness determination assigning a variable predictiveness value to each variable in the population of candidate variables; selecting a plurality of variables from the population of candidate variables as a selected set based on the variable predictiveness values of the variables in the population of candidate variables, variables not in the selected set being members of a rejected set; generating a plurality of derived variables based on variables in the rejected set without consideration of any variables in the selected set; performing a derived variable predictiveness determination on the plurality of derived variables using the processor, the derived variable predictiveness determination assigning a derived variable predictiveness value to each derived variable; selecting one or more derived variables as selected derived variables based on the derived variable predictiveness values of the derived variables; and storing the selected set and the one or more selected derived variables in a computer-readable memory as the model input variables for the data model.