# US 2014/0207267 A1 · Metric based on estimate value

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- Number: US 2014/0207267 A1
- Document ID: US20140207267A1
- Status: Published application
- Kind: A1
- Application number: 13/748,198
- Filed: 2013-01-23
- Published: 2014-07-24
- Earliest claimed: 2013-01-23
- Assignees: Hewlett-Packard Development Company, L.P.
- Applicants: Hewlett-Packard Development Company, L.P.
- Inventors: Kemal GULER; Jianqiang WANG
- Web page: https://patentgrep.com/patent/US20140207267A1

## CPC

- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## 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.

## Claims

### Claim 1

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.

### Claim 2 (depends on 1)

The method of claim 1 wherein the loss function includes an argument, and the loss function evaluates to a product of the argument and a penalty value when the argument is greater than or equal to a threshold.

### Claim 3 (depends on 2)

The method of claim 2 wherein the argument is a difference between the actual value and the estimate value.

### Claim 4 (depends on 2)

The method of claim 2 wherein the loss function evaluates to a product of the argument and a quantity of the penalty minus one when the argument is less than the threshold.

### Claim 5 (depends on 4)

The method of claim 4 wherein the threshold is zero.

### Claim 6 (depends on 1)

The method of claim 1 further comprising computing multiple ratios of the loss function and the weighted sum, each ratio being for a pair of estimate and actual values for a different event.

### Claim 7 (depends on 6)

The method of claim 6 wherein computing the metric comprises computing a mean or median of the multiple ratios.

### Claim 8

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.

### Claim 9 (depends on 8)

The non-transitory, computer-readable storage device of claim 8 wherein the instructions further cause the processor to compute multiple ratios of the loss function and the weighted sum, each ratio being for a different pair of estimate and actual values.

### Claim 10 (depends on 9)

The non-transitory, computer-readable storage device of claim 9 wherein the instructions cause the processor to compute the metric by computing a mean or median of the ratios.

### Claim 11 (depends on 8)

The non-transitory, computer-readable storage device of claim 8 wherein the loss function includes an argument, and the loss function evaluates to a product of the argument and a penalty value when the argument is greater than or equal to a threshold.

### Claim 12 (depends on 11)

The non-transitory, computer-readable storage device of claim 11 wherein the loss function evaluates to a product of the argument and a quantity of the penalty minus one when the argument is less than the threshold.

### Claim 13 (depends on 11)

The non-transitory, computer-readable storage device of claim 11 wherein the argument is a difference between the actual value and the estimate value.

### Claim 14 (depends on 12)

The non-transitory, computer-readable storage device of claim 12 wherein the threshold is zero.

### Claim 15

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.

### Claim 16 (depends on 15)

The system of claim 15 wherein the AMASE estimation engine is to compute multiple ratios of the loss function and the weighted sum, each ratio being for a different pair of estimate and actual values.

### Claim 17 (depends on 16)

The system of claim 16 wherein the AMASE estimation engine is to compute the performance metric by computing a mean or median of the ratios.

### Claim 18 (depends on 15)

The system of claim 15 wherein the loss function includes an argument, and the loss function evaluates to a product of the argument and a penalty value when the argument is greater than or equal to a threshold.

### Claim 19 (depends on 18)

The system of claim 18 wherein the loss function evaluates to a product of the argument and a quantity of the penalty minus one when the argument is less than the threshold.

### Claim 20 (depends on 18)

The system of claim 18 wherein the argument is a difference between the actual value and the estimate value.