About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

incrmmda

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute a moving [mean directional accuracy][mean-directional-accuracy] (MDA) incrementally.

For a window of size W, the [mean directional accuracy][mean-directional-accuracy] is defined as

Equation for the mean directional accuracy.

where f_i is the forecast value, a_i is the actual value, sgn(x) is the [signum][@stdlib/math/base/special/signum] function, and δ is the [Kronecker delta][@stdlib/math/base/special/kronecker-delta].

Installation

``bash npm install @stdlib/stats-incr-mmda`

`Usage`

`javascript var incrmmda = require( '@stdlib/stats-incr-mmda' );`

#### incrmmda( window )

Returns an accumulator function which incrementally computes a moving [mean directional accuracy][mean-directional-accuracy]. The window parameter defines the number of values over which to compute the moving [mean directional accuracy][mean-directional-accuracy].

`javascript var accumulator = incrmmda( 3 );`

#### accumulator( \[f, a] )

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

`javascript var accumulator = incrmmda( 3 );

var m = accumulator(); // returns null

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

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

m = accumulator( 3.0, 9.0 ); // [(+,+), (-,+), (+,+)] // returns ~0.67

// Window begins sliding... m = accumulator( 7.0, 3.0 ); // [(-,+), (+,+), (+,-)] // returns ~0.33

m = accumulator( 5.0, 3.0 ); // [(+,+), (+,-), (-,0)] // returns ~0.33

m = accumulator(); // returns ~0.33`

`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-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. - AsW (f,a) 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.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var incrmmda = require( '@stdlib/stats-incr-mmda' );

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

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

// For each simulated datum, update the moving 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() );`

*

`Notice`

This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

`License`

See [LICENSE][stdlib-license].

`Copyright`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-incr-mmda.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-incr-mmda

[test-image]: https://github.com/stdlib-js/stats-incr-mmda/actions/workflows/test.yml/badge.svg?branch=v0.2.3 [test-url]: https://github.com/stdlib-js/stats-incr-mmda/actions/workflows/test.yml?query=branch:v0.2.3

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-incr-mmda/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-incr-mmda?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/stats-incr-mmda/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-incr-mmda/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-incr-mmda/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-incr-mmda/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-incr-mmda/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-incr-mmda/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-incr-mmda/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-incr-mmda/main/LICENSE

[mean-directional-accuracy]: https://en.wikipedia.org/wiki/Mean_Directional_Accuracy_%28MDA%29

[@stdlib/math/base/special/signum]: https://www.npmjs.com/package/@stdlib/math-base-special-signum

[@stdlib/math/base/special/kronecker-delta]: https://www.npmjs.com/package/@stdlib/math-base-special-kronecker-delta

[@stdlib/stats/incr/mda]: https://www.npmjs.com/package/@stdlib/stats-incr-mda

[@stdlib/stats/incr/mmape]: https://www.npmjs.com/package/@stdlib/stats-incr-mmape


  
    About stdlib...
  

  We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

  The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

  When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`incrmmda`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> Compute a moving [mean directional accuracy][mean-directional-accuracy] (MDA) incrementally.

For a window of size W, the [mean directional accuracy][mean-directional-accuracy] is defined as

where f_i is the forecast value, a_i is the actual value, sgn(x) is the [signum][@stdlib/math/base/special/signum] function, and δ is the [Kronecker delta][@stdlib/math/base/special/kronecker-delta].

`Installation`

``bash npm install @stdlib/stats-incr-mmda `

`Usage`

`javascript var incrmmda = require( '@stdlib/stats-incr-mmda' ); `

#### incrmmda( window )

Returns an accumulator function which incrementally computes a moving [mean directional accuracy][mean-directional-accuracy]. The window parameter defines the number of values over which to compute the moving [mean directional accuracy][mean-directional-accuracy].

`javascript var accumulator = incrmmda( 3 ); `

#### accumulator( \[f, a] )

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

`javascript var accumulator = incrmmda( 3 );

var m = accumulator(); // returns null

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

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

m = accumulator( 3.0, 9.0 ); // [(+,+), (-,+), (+,+)] // returns ~0.67

// Window begins sliding... m = accumulator( 7.0, 3.0 ); // [(-,+), (+,+), (+,-)] // returns ~0.33

m = accumulator( 5.0, 3.0 ); // [(+,+), (+,-), (-,0)] // returns ~0.33

m = accumulator(); // returns ~0.33 `

`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 (f,a) 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.

`Examples`

`javascript var randu = require( '@stdlib/random-base-randu' ); var incrmmda = require( '@stdlib/stats-incr-mmda' );

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

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

// For each simulated datum, update the moving 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() ); `