incrmda

Compute the mean directional accuracy (MDA) incrementally.

The mean directional accuracy is defined as

where f_i is the forecast value, a_i is the actual value, sgn(x) is the signum function, and δ is the Kronecker delta.

Installation

npm install @stdlib/stats-incr-mda

Usage

var incrmda = require( '@stdlib/stats-incr-mda' );

incrmda()

Returns an accumulator function which incrementally computes the mean directional accuracy.

var accumulator = incrmda();

accumulator( [f, a] )

If provided input values f and a, the accumulator function returns an updated mean directional accuracy. If not provided input values f and a, the accumulator function returns the current mean directional accuracy.

var accumulator = incrmda();

var m = accumulator( 2.0, 3.0 );
// returns 1.0

m = accumulator( -1.0, 4.0 );
// returns 0.5

m = accumulator( -3.0, -2.0 );
// returns ~0.67

m = accumulator();
// returns ~0.67

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 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.

Examples

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

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

// Initialize an accumulator:
accumulator = incrmda();

// For each simulated datum, update the mean directional accuracy...
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/mape: compute the mean absolute percentage error (MAPE) incrementally.
• @stdlib/stats-incr/mmda: compute a moving mean directional accuracy (MDA) 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.