@stdlib/stats-incr-maape NPM

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 in NaN, the accumulated value is NaN 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.

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.