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Compute a moving mean arctangent absolute percentage error (MAAPE) incrementally.
For a window of size W
, the mean arctangent absolute percentage error is defined as
where f_i
is the forecast value and a_i
is the actual value.
npm install @stdlib/stats-incr-mmaape
var incrmmaape = require( '@stdlib/stats-incr-mmaape' );
Returns an accumulator function
which incrementally computes a moving mean arctangent absolute percentage error. The window
parameter defines the number of values over which to compute the moving mean arctangent absolute percentage error.
var accumulator = incrmmaape( 3 );
If provided input values f
and a
, the accumulator function returns an updated mean arctangent absolute percentage error. If not provided input values f
and a
, the accumulator function returns the current mean arctangent absolute percentage error.
var accumulator = incrmmaape( 3 );
var m = accumulator();
// returns null
// Fill the window...
m = accumulator( 2.0, 3.0 ); // [(2.0,3.0)]
// returns ~0.32
m = accumulator( 1.0, 4.0 ); // [(2.0,3.0), (1.0,4.0)]
// returns ~0.48
m = accumulator( 3.0, 9.0 ); // [(2.0,3.0), (1.0,4.0), (3.0,9.0)]
// returns ~0.52
// Window begins sliding...
m = accumulator( 7.0, 3.0 ); // [(1.0,4.0), (3.0,9.0), (7.0,3.0)]
// returns ~0.72
m = accumulator( 5.0, 3.0 ); // [(3.0,9.0), (7.0,3.0), (5.0,3.0)]
// returns ~0.70
m = accumulator();
// returns ~0.70
- Input values are not type checked. If provided
NaN
or a value which, when used in computations, results inNaN
, the accumulated value isNaN
for at leastW-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 firstW-1
returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values. - Note that, unlike the mean absolute percentage error (MAPE), the mean arctangent absolute percentage error is expressed in radians on the interval [0,π/2].
var randu = require( '@stdlib/random-base-randu' );
var incrmmaape = require( '@stdlib/stats-incr-mmaape' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmmaape( 5 );
// For each simulated datum, update the moving mean arctangent 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() );
- Kim, Sungil, and Heeyoung Kim. 2016. "A new metric of absolute percentage error for intermittent demand forecasts." International Journal of Forecasting 32 (3): 669–79. doi:10.1016/j.ijforecast.2015.12.003.
-
@stdlib/stats-incr/maape
: compute the mean arctangent absolute percentage error (MAAPE) incrementally. -
@stdlib/stats-incr/mmape
: compute a moving mean absolute percentage error (MAPE) incrementally. -
@stdlib/stats-incr/mmpe
: compute a moving mean percentage error (MPE) incrementally. -
@stdlib/stats-incr/mmean
: compute a moving arithmetic mean incrementally.
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
See LICENSE.
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