1. A method of implementing a forecasting cohort, the method comprising: generating, by one or more processors, a forecasting cohort of forecasting algorithms, wherein the forecasting cohort comprises a first set of forecasting algorithms of a first type of forecasting algorithm, and wherein the forecasting cohort further comprises a second set of forecasting algorithms of a second type of forecasting algorithm; determining, by one or more processors, an initial confidence level and a half-life of each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein the initial confidence level describes an accuracy level of each of the forecasting algorithms at an initial time in predicting a condition, and wherein the half-life describes a subsequent time at which a forecasting algorithm has reached half of its forecast horizon; determining, by one or more processors, a half-life weight for each of the first set of forecasting algorithms and the second set of forecasting algorithms at a subsequent time that is subsequent to the initial time, wherein half-life weights decrease an effect of a forecasting algorithm as time elapses; determining, by one or more processors, a combined confidence level of the forecasting cohort at the subsequent time, wherein the combined confidence level is based on the initial confidence level and the half-life weight of each of the first set of forecasting algorithms and the second set of forecasting algorithms; and utilizing, by one or more processors, the combined confidence level of the forecasting cohort at the subsequent time to adjust resource usage.