Metric based on estimate value
AssigneeHewlett-Packard Development Company, L.P.
InventorsKemal GULER, Jianqiang WANG
Life of this patent
Estimating the term…
Abstract
A method includes obtaining an estimate value generated by an estimation process and obtaining an actual value corresponding to the estimate value. The method further includes computing a metric based on a ratio of a loss function and a weighted sum of the estimate value and the actual value. The loss function includes a penalty parameter to control a relative penalty of positive and negative prediction errors.
Claims20
A method, comprising:
- obtaining, by an estimate value engine, an estimate value generated by an estimation process;
- obtaining, by an actual value engine, an actual value corresponding to the estimate value;
- and
- computing, by an asymmetric mean absolute scaled error (AMASE) performance metric engine, a metric based on a ratio of a loss function and a weighted sum of the estimate value and the actual value, the loss function including a penalty parameter to control a relative penalty of positive and negative prediction errors.
A non-transitory, computer-readable storage device including instructions that are executable by a processor and, when executed, cause the compute to:
- obtain an estimate value generated by an estimation process;
- obtain an actual value corresponding to the estimate value;
- and
- compute a ratio of a loss function and a weighted sum of the estimate value and the actual value, the loss function including a user-specified penalty parameter that permits the user to control a relative penalty of positive and negative prediction errors.
A system, comprising:
- an asymmetric mean absolute scaled error (AMASE) estimation engine to receive a first parameter as well as an estimate value for an event and a corresponding actual value for the events, and to compute a performance metric by computing a ratio of a loss function and a weighted sum of a pair of corresponding estimate and actual values;
- wherein the loss function includes the first parameter that permits control of a relative penalty of positive and negative prediction errors.
Dependencies are inferred from the claim text.