Cohort half life forecasting combination from a confident jury
First Claim
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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.
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Abstract
A forecasting cohort includes a first set of forecasting algorithms and a second set of forecasting algorithms. An initial confidence level and 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 combined confidence level of the forecasting cohort at the subsequent time is determined and used to adjust resource usage.
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Citations
20 Claims
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1. A method of implementing a forecasting cohort, the method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer program product for implementing a forecasting cohort, the computer program product comprising a computer readable storage medium having program code embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, and wherein the program code is readable and executable by a processor to perform a method comprising:
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generating 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 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 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 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 the combined confidence level of the forecasting cohort at the subsequent time to adjust resource usage. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
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20. A device comprising:
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a sensor, wherein the sensor develops sensor readings from the device that describe current conditions of the device; one or more processors for generating 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; one or more processors for determining 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; one or more processors for determining 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; one or more processors for determining 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; one or more processors for predicting future usage of the resources by the device based on the combined confidence level of the forecasting cohort; and one or more processors for reallocating resources based on the predicted future usage, wherein the half-life weight is calculated according to;
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Specification