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Compute a moving mean error (ME) incrementally.
For a window of size W, the mean error is defined as
import incrmme from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-mme@esm/index.mjs';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 );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- Input values are not type checked. If provided
NaNor a value which, when used in computations, results inNaN, the accumulated value isNaNfor at leastW-1future 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 firstW-1returned 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.
<!DOCTYPE html>
<html lang="en">
<body>
<script type="module">
import randu from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@esm/index.mjs';
import incrmme from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-incr-mme@esm/index.mjs';
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() );
</script>
</body>
</html>@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.
This package is part of stdlib, a standard library 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.
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