1. A method of predicting a future event associated with a business based on historical data associated with a model of past events of the business, comprising: obtaining, at a processor-based system, a plurality of modeling variables associated with the model, a dependent variable associated with the model and dependent on the plurality of modeling variables, and the historical data associated with the model; performing, using the processor-based system, a transformation of the plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable; selecting, using the processor-based system, a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on at least one of: (i) a correlation between a transformed variable and the dependent variable, and (ii) a proportion of a range of the dependent variable explained by the transformed variable; or a selection rule based on a log-likelihood difference; determining, using the processor-based system, a set of prediction variables, wherein the determining is based on a regression of the subset of the plurality of transformed variables; and generating, using the processor-based system, a predictive model using the prediction variables obtained from the regression, wherein the predictive model is used by the processing system to predict the future event.
1. A method of predicting a future event associated with a business based on historical data associated with a model of past events of the business, comprising: obtaining, at a processor-based system, a plurality of modeling variables associated with the model, a dependent variable associated with the model and dependent on the plurality of modeling variables, and the historical data associated with the model; performing, using the processor-based system, a transformation of the plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable; selecting, using the processor-based system, a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on at least one of: (i) a correlation between a transformed variable and the dependent variable, and (ii) a proportion of a range of the dependent variable explained by the transformed variable; or a selection rule based on a log-likelihood difference; determining, using the processor-based system, a set of prediction variables, wherein the determining is based on a regression of the subset of the plurality of transformed variables; and generating, using the processor-based system, a predictive model using the prediction variables obtained from the regression, wherein the predictive model is used by the processing system to predict the future event. 3. The method of claim 1 , further comprising: performing substitution of values in the historical data associated with the model, the substitution being performed for at least one of missing values, invalid values, or zero values.