incrmme

Compute a moving mean error (ME) incrementally.

For a window of size W, the mean error is defined as

Installation

npm install @stdlib/stats-incr-mme

Usage

var incrmme = require( '@stdlib/stats-incr-mme' );

incrmme( window )

Returns an accumulator function which incrementally computes a moving mean error. The window parameter defines the number of values over which to compute the moving mean error.

var accumulator = incrmme( 3 );

accumulator( [x, y] )

If provided input values x and y, the accumulator function returns an updated mean error. If not provided input values x and y, the accumulator function returns the current mean error.

var accumulator = incrmme( 3 );

var m = accumulator();
// returns null

// Fill the window...
m = accumulator( 2.0, 3.0 ); // [(2.0,3.0)]
// returns 1.0

m = accumulator( -1.0, 4.0 ); // [(2.0,3.0), (-1.0,4.0)]
// returns 3.0

m = accumulator( 3.0, 9.0 ); // [(2.0,3.0), (-1.0,4.0), (3.0,9.0)]
// returns 4.0

// Window begins sliding...
m = accumulator( -7.0, 3.0 ); // [(-1.0,4.0), (3.0,9.0), (-7.0,3.0)]
// returns 7.0

m = accumulator( -5.0, -3.0 ); // [(3.0,9.0), (-7.0,3.0), (-5.0,-3.0)]
// returns 6.0

m = accumulator();
// returns 6.0

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 at least W-1 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.
• As W (x,y) pairs are needed to fill the window buffer, the first W-1 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 error as errors can cancel. This stated, that errors can cancel makes the mean error suitable for measuring the bias in forecasts.
• Warning: the mean error is scale-dependent and, thus, the measure should not be used to make comparisons between datasets having different scales.

Examples

var randu = require( '@stdlib/random-base-randu' );
var incrmme = require( '@stdlib/stats-incr-mme' );

var accumulator;
var v1;
var v2;
var i;

// Initialize an accumulator:
accumulator = incrmme( 5 );

// For each simulated datum, update the moving mean 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/stats-incr/me: compute the mean error (ME) incrementally.
• @stdlib/stats-incr/mmae: compute a moving mean absolute error (MAE) incrementally.
• @stdlib/stats-incr/mmean: compute a moving arithmetic mean 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.