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, wellwritten, studied, documented, tested, measured, and highquality 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!
Compute the mean absolute percentage error (MAPE) incrementally.
The mean absolute percentage error is defined as
where f_i
is the forecast value and a_i
is the actual value.
npm install @stdlib/statsincrmape
var incrmape = require( '@stdlib/statsincrmape' );
Returns an accumulator function
which incrementally computes the mean absolute percentage error.
var accumulator = incrmape();
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 = incrmape();
var m = accumulator( 2.0, 3.0 );
// returns ~33.33
m = accumulator( 1.0, 4.0 );
// returns ~54.17
m = accumulator( 3.0, 5.0 );
// returns ~49.44
m = accumulator();
// returns ~49.44

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 nonnumeric inputs are possible, you are advised to type check and handle accordingly before passing the value to the accumulator function. 
Warning: the mean absolute percentage error has several shortcomings:
 The measure is not suitable for intermittent demand patterns (i.e., when
a_i
is0
).  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 not suitable for intermittent demand patterns (i.e., when
var randu = require( '@stdlib/randombaserandu' );
var incrmape = require( '@stdlib/statsincrmape' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmape();
// For each simulated datum, update the 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() );

@stdlib/statsincr/maape
: compute the mean arctangent absolute percentage error (MAAPE) incrementally. 
@stdlib/statsincr/mae
: compute the mean absolute error (MAE) incrementally. 
@stdlib/statsincr/mean
: compute an arithmetic mean incrementally. 
@stdlib/statsincr/mmape
: compute a moving mean absolute percentage error (MAPE) 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.
Copyright © 20162024. The Stdlib Authors.