3.9 KiB
3.9 KiB
incrmpe
Compute the mean percentage error (MPE) incrementally.
The mean percentage error is defined as
where f_i
is the forecast value and a_i
is the actual value.
Usage
var incrmpe = require( '@stdlib/stats/incr/mpe' );
incrmpe()
Returns an accumulator function
which incrementally computes the mean percentage error.
var accumulator = incrmpe();
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 = incrmpe();
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
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 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. - 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/random/base/randu' );
var incrmpe = require( '@stdlib/stats/incr/mpe' );
var accumulator;
var v1;
var v2;
var i;
// Initialize an accumulator:
accumulator = incrmpe();
// For each simulated datum, update the 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() );