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About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

incrmaape

NPM version Build Status Coverage Status

Compute the mean arctangent absolute percentage error (MAAPE) incrementally.

The mean arctangent absolute percentage error is defined as

$$\mathop{\mathrm{MAAPE}} = \frac{1}{n} \sum_{i=0}^{n-1} \mathop{\mathrm{arctan}}\biggl( \biggl| \frac{a_i - f_i}{a_i} \biggr| \biggr)$$

where f_i is the forecast value and a_i is the actual value.

Usage

To use in Observable,

incrmaape = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-maape@umd/browser.js' )

To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:

var incrmaape = require( 'path/to/vendor/umd/stats-incr-maape/index.js' )

To include the bundle in a webpage,

<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-maape@umd/browser.js"></script>

If no recognized module system is present, access bundle contents via the global scope:

<script type="text/javascript">
(function () {
    window.incrmaape;
})();
</script>

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

<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-maape@umd/browser.js"></script>
<script type="text/javascript">
(function () {

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() );

})();
</script>
</body>
</html>

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

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

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.