incrmape
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.
Usage
var incrmape = require( '@stdlib/stats/incr/mape' );
incrmape()
Returns an accumulator function
which incrementally computes the mean absolute percentage error.
var accumulator = incrmape();
accumulator( [f, a] )
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
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.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
Examples
var randu = require( '@stdlib/random/base/randu' );
var incrmape = require( '@stdlib/stats/incr/mape' );
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() );