# incrmmape

Compute a moving mean absolute percentage error incrementally.

For a window of size `W`

, the mean absolute percentage error is defined as

where `f_i`

is the forecast value and `a_i`

is the actual value.

## Usage

```
var incrmmape = require( '@stdlib/stats/incr/mmape' );
```

#### incrmmape( window )

Returns an accumulator `function`

which incrementally computes a moving mean absolute percentage error. The `window`

parameter defines the number of values over which to compute the moving mean absolute percentage error.

```
var accumulator = incrmmape( 3 );
```

#### accumulator( [f, a] )

If provided input values `f`

and `a`

, the accumulator function returns an updated mean absolute percentage error. If not provided input values `f`

and `a`

, the accumulator function returns the current mean absolute percentage error.

```
var accumulator = incrmmape( 3 );
var m = accumulator();
// returns null
// Fill the window...
m = accumulator( 2.0, 3.0 ); // [(2.0,3.0)]
// returns ~33.33
m = accumulator( 1.0, 4.0 ); // [(2.0,3.0), (1.0,4.0)]
// returns ~54.17
m = accumulator( 3.0, 9.0 ); // [(2.0,3.0), (1.0,4.0), (3.0,9.0)]
// returns ~58.33
// Window begins sliding...
m = accumulator( 7.0, 3.0 ); // [(1.0,4.0), (3.0,9.0), (7.0,3.0)]
// returns ~91.67
m = accumulator( 5.0, 3.0 ); // [(3.0,9.0), (7.0,3.0), (5.0,3.0)]
// returns ~88.89
m = accumulator();
// returns ~88.89
```

## Notes

Input values are

**not**type checked. If provided`NaN`

or a value which, when used in computations, results in`NaN`

, the accumulated value is`NaN`

for**at least**`W-1`

future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly**before**passing the value to the accumulator function.As

`W`

(f,a) pairs are needed to fill the window buffer, the first`W-1`

returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.**Warning**: the mean absolute percentage error has several shortcomings:- The measure is
**not**suitable for intermittent demand patterns (i.e., when`a_i`

is`0`

). - The mean absolute percentage error is not symmetrical, as the measure cannot exceed 100% for forecasts which are too "low" and has no limit for forecasts which are too "high".
- When used to compare the accuracy of forecast models (e.g., predicting demand), the measure is biased toward forecasts which are too low.

- The measure is

## Examples

```
var randu = require( '@stdlib/random/base/randu' );
var incrmmape = require( '@stdlib/stats/incr/mmape' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmmape( 5 );
// For each simulated datum, update the moving mean absolute percentage error...
for ( i = 0; i < 100; i++ ) {
v1 = ( randu()*100.0 ) + 50.0;
v2 = ( randu()*100.0 ) + 50.0;
accumulator( v1, v2 );
}
console.log( accumulator() );
```