1. A computer implemented method for project prediction, comprising: obtaining historical project data of historical projects; analyzing, with an analyzer stored as a set of computer readable instructions on a computer readable storage media and executable by a processor, the historical project data to generate at least one model for predicting a proposed project cycle time, wherein the analyzing to generate the at least one model comprises: identifying at least one transition data point in the historical project data corresponding to a difference between actual cycle times for the historical projects that is indicative of a difference in the nature of the historical projects on either side of the at least one transition data point, wherein identifying the at least one transition data point comprises arranging projects in order based on actual cycle times for the historical projects, where the difference between actual cycle times for the historical projects corresponds to a transition data point where the actual cycle times transition from a first trend in the actual cycle times to a second trend in the actual cycle times; dividing the historical project data into historical data ranges, one historical data range approximately on either side of the at least one transition data point; and generating a linear sub-model for predicting project cycle time for each of the historical data ranges using multiple regression analysis on characteristics of the historical projects in the historical data range; selecting the at least one model for predicting the proposed project cycle time, wherein the selected model comprises the linear sub-models corresponding to the historical data ranges; and applying proposed project data to one of the linear sub-models to predict the proposed project cycle time, wherein a proposed data range of the proposed project corresponds with the historical data range corresponding with the one of the linear sub-models.