@stdlib/stats-incr-maape
v0.2.2
Published
Compute the mean arctangent absolute percentage error (MAAPE) incrementally.
Downloads
195
Readme
incrmaape
Compute the mean arctangent absolute percentage error (MAAPE) incrementally.
The mean arctangent absolute percentage error is defined as
where f_i
is the forecast value and a_i
is the actual value.
Installation
npm install @stdlib/stats-incr-maape
Usage
var incrmaape = require( '@stdlib/stats-incr-maape' );
incrmaape()
Returns an accumulator function
which incrementally computes the mean arctangent absolute percentage error.
var accumulator = incrmaape();
accumulator( [f, a] )
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 = incrmaape();
var m = accumulator( 2.0, 3.0 );
// returns ~0.3218
m = accumulator( 1.0, 4.0 );
// returns ~0.4826
m = accumulator( 3.0, 5.0 );
// returns ~0.4486
m = accumulator();
// returns ~0.4486
Notes
- Input values are not type checked. If provided
NaN
or a value which, when used in computations, results inNaN
, the accumulated value isNaN
for all 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. - 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].
Examples
var randu = require( '@stdlib/random-base-randu' );
var incrmaape = require( '@stdlib/stats-incr-maape' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmaape();
// For each simulated datum, update the 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() );
References
- 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.
See Also
@stdlib/stats-incr/mae
: compute the mean absolute error (MAE) incrementally.@stdlib/stats-incr/mape
: compute the mean absolute percentage error (MAPE) incrementally.@stdlib/stats-incr/mean
: compute an arithmetic mean incrementally.@stdlib/stats-incr/mmaape
: compute a moving arctangent mean absolute percentage error (MAAPE) incrementally.
Notice
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.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.