A forecasting cohort includes a first set of forecasting algorithms and a second set of forecasting algorithms. A half-life of each of the first set of forecasting algorithms and the second set of forecasting algorithms are determined. A half-life weight for each of the first set of forecasting algorithms and the second set of forecasting algorithms at a subsequent time are determined, such that the half-life weights decrease an effect of a forecasting algorithm as time elapses. A confidence level of the forecasting cohort at the subsequent time is determined and used to adjust resource usage.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A 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, a half-life of each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein the half-life describes a subsequent time at which a forecasting algorithm has reached half of its forecast horizon as measured from an initial time; 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, wherein half-life weights decrease an effect of a forecasting algorithm as time elapses; determining, by one or more processors, a confidence level of the forecasting cohort at the subsequent time, wherein the confidence level is based on the half-life weight of each of the first set of forecasting algorithms and the second set of forecasting algorithms; utilizing, by one or more processors, the confidence level of the forecasting cohort at the subsequent time to adjust resource usage; receiving, by one or more processors, sensor readings from a device, wherein the sensor readings describe current conditions of the device, and wherein the device includes resources; predicting, by one or more processors, future usage of the resources in the device based on the confidence level of the forecasting cohort; and reallocating, by one or more processors, the resources used by the device based on the predicted future usage. 2. The method of claim 1, wherein the resources used by the device are software resources. 3. The method of claim 1, wherein the resources used by the device are hardware resources. 4. The method of claim 1, wherein reallocated resources are in the device. 5. The method of claim 1, wherein the sensor readings are received at the initial time. 6. The method of claim 1, wherein the sensor readings are received at the subsequent time. 7. The method of claim 1, wherein the half-life weight of a forecasting algorithm is: 2 - m r h 1 / 2 * α where mr is an amount of time that has passed since the initial time, h1/2 is a half-life of the forecasting algorithm, and α is a correction factor derived from historical data for the half-life weight, and wherein a combined confidence level is calculated according to: ∑ x = t  T   ∑ f = 0  f   f f  w x * f f  ( x ) where ∑ x = t  T  is a sum of all time slices taken during a life of the forecasting cohort, where ∑ f = 0  f   f f  w x is a sum of all forecasting algorithm weights, where ffwx represents all half-life weights of the forecasting algorithms, and where ff(x) represents all forecasting algorithms in the forecasting cohort. 8. The method of claim 1, wherein the resource usage is predicted at the initial time. 9. The method of claim 1, wherein the resource usage is predicted at the subsequent time. 10. A computer program product comprising one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums, the stored program instructions comprising: program instructions to generate 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; program instructions to determine a half-life of each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein the half-life describes a subsequent time at which a forecasting algorithm has reached half of its forecast horizon as measured from an initial time; program instructions to determine a half-life weight for each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein half-life weights decrease an effect of a forecasting algorithm as time elapses; program instructions to determine a confidence level of the forecasting cohort at the subsequent time, wherein the confidence level is based on the half-life weight of each of the first set of forecasting algorithms and the second set of forecasting algorithms; program instructions to utilize the confidence level of the forecasting cohort at the subsequent time to adjust resource usage; program instructions to receive sensor readings from a device, wherein the sensor readings describe current conditions of the device, and wherein the device includes resources; program instructions to predict future usage of the resources in the device based on the confidence level of the forecasting cohort; and program instructions to reallocate the resources used by the device based on the predicted future usage. 11. The computer program product of claim 10, wherein the resources used by the device are software resources. 12. The computer program product of claim 10, wherein the resources used by the device are hardware resources. 13. The computer program product of claim 10, wherein reallocated resources are in the device. 14. The computer program product of claim 10, wherein the sensor readings are received at the initial time. 15. The computer program product of claim 10, wherein the half-life weight of a forecasting algorithm is: 2 - m r h 1 / 2 * α where mr is an amount of time that has passed since the initial time, h1/2 is a half-life of the forecasting algorithm, and α is a correction factor derived from historical data for the half-life weight, and wherein a combined confidence level is calculated according to: ∑ x = t  T   ∑ f = 0  f   f f  w x * f f  ( x ) where ∑ x = t  T  is a sum of all time slices taken during a life of the forecasting cohort, where ∑ f = 0  f   f f  w x is a sum of all forecasting algorithm weights, where ffwx represents all half-life weights of the forecasting algorithms, and where ff(x) represents all forecasting algorithms in the forecasting cohort. 16. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising: program instructions to generate 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; program instructions to determine a half-life of each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein the half-life describes a subsequent time at which a forecasting algorithm has reached half of its forecast horizon as measured from an initial time; program instructions to determine a half-life weight for each of the first set of forecasting algorithms and the second set of forecasting algorithms, wherein half-life weights decrease an effect of a forecasting algorithm as time elapses; program instructions to determine a confidence level of the forecasting cohort at the subsequent time, wherein the confidence level is based on the half-life weight of each of the first set of forecasting algorithms and the second set of forecasting algorithms; program instructions to utilize the confidence level of the forecasting cohort at the subsequent time to adjust resource usage; program instructions to receive sensor readings from a device, wherein the sensor readings describe current conditions of the device, and wherein the device includes resources; program instructions to predict future usage of the resources in the device based on the confidence level of the forecasting cohort; and program instructions to reallocate the resources used by the device based on the predicted future usage. 17. The computer system of claim 16, wherein the resources used by the device are software resources. 18. The computer system of claim 16, wherein the resources used by the device are hardware resources. 19. The computer system of claim 16, wherein reallocated resources are in the device. 20. The computer system of claim 16, wherein the half-life weight of a forecasting algorithm is: 2 - m r h 1 / 2 * α where mr is an amount of time that has passed since the initial time, h1/2 is a half-life of the forecasting algorithm, and α is a correction factor derived from historical data for the half-life weight, and wherein a combined confidence level is calculated according to: ∑ x = t  T   ∑ f = 0  f   f f  w x * f f  ( x ) where ∑ x = t  T  is a sum of all time slices taken during a life of the forecasting cohort, where ∑ f = 0  f   f f  w x is a sum of all forecasting algorithm weights, where ffwx represents all half-life weights of the forecasting algorithms, and where ff(x) represents all forecasting algorithms in the forecasting cohort.