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incrmmpe
Compute a moving mean percentage error (MPE) incrementally.
For a window of size W
, the mean percentage error is defined as
where f_i
is the forecast value and a_i
is the actual value.
Installation
npm install @stdlib/statsincrmmpe
Usage
var incrmmpe = require( '@stdlib/statsincrmmpe' );
incrmmpe( window )
Returns an accumulator function
which incrementally computes a moving mean percentage error. The window
parameter defines the number of values over which to compute the moving mean percentage error.
var accumulator = incrmmpe( 3 );
accumulator( [f, a] )
If provided input values f
and a
, the accumulator function returns an updated mean percentage error. If not provided input values f
and a
, the accumulator function returns the current mean percentage error.
var accumulator = incrmmpe( 3 );
var m = accumulator();
// returns null
// Fill the window...
m = accumulator( 2.0, 3.0 ); // [(2.0,3.0)]
// returns ~33.33
m = accumulator( 1.0, 4.0 ); // [(2.0,3.0), (1.0,4.0)]
// returns ~54.17
m = accumulator( 3.0, 9.0 ); // [(2.0,3.0), (1.0,4.0), (3.0,9.0)]
// returns ~58.33
// Window begins sliding...
m = accumulator( 7.0, 3.0 ); // [(1.0,4.0), (3.0,9.0), (7.0,3.0)]
// returns ~2.78
m = accumulator( 5.0, 3.0 ); // [(3.0,9.0), (7.0,3.0), (5.0,3.0)]
// returns ~44.44
m = accumulator();
// returns ~44.44
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 at leastW1
future invocations. If nonnumeric 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 firstW1
returned values are calculated from smaller sample sizes. Until the window is full, each returned value is calculated from all provided values.  Be careful when interpreting the mean percentage error as errors can cancel. This stated, that errors can cancel makes the mean percentage error suitable for measuring the bias in forecasts.

Warning: the mean percentage error is not suitable for intermittent demand patterns (i.e., when
a_i
is0
). Interpretation is most straightforward when actual and forecast values are positive valued (e.g., number of widgets sold).
Examples
var randu = require( '@stdlib/randombaserandu' );
var incrmmpe = require( '@stdlib/statsincrmmpe' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmmpe( 5 );
// For each simulated datum, update the moving mean 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() );
See Also

@stdlib/statsincr/mmape
: compute a moving mean absolute percentage error (MAPE) incrementally. 
@stdlib/statsincr/mme
: compute a moving mean error (ME) incrementally. 
@stdlib/statsincr/mpe
: compute the mean percentage error (MPE) 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 © 20162024. The Stdlib Authors.