time-to-botec

README.md (5030B)

---

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 # incrmmape
 
 > Compute a moving [mean absolute percentage error][mean-absolute-percentage-error] incrementally.
 
 <section class="intro">
 
 For a window of size `W`, the [mean absolute percentage error][mean-absolute-percentage-error] is defined as
 
 <!-- <equation class="equation" label="eq:mean_absolute_percentage_error" align="center" raw="\operatorname{MAPE}  = \frac{100}{W} \sum_{i=0}^{W-1} \biggl| \frac{a_i - f_i}{a_i} \biggr|" alt="Equation for the mean absolute percentage error."> -->
 
 <div class="equation" align="center" data-raw-text="\operatorname{MAPE}  = \frac{100}{W} \sum_{i=0}^{W-1} \biggl| \frac{a_i - f_i}{a_i} \biggr|" data-equation="eq:mean_absolute_percentage_error">
     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@f13330c8d3bb837438b30381f6cd70dbccd4837c/lib/node_modules/@stdlib/stats/incr/mmape/docs/img/equation_mean_absolute_percentage_error.svg" alt="Equation for the mean absolute percentage error.">
     <br>
 </div>
 
 <!-- </equation> -->
 
 where `f_i` is the forecast value and `a_i` is the actual value.
 
 </section>
 
 <!-- /.intro -->
 
 <section class="usage">
 
 ## Usage
 
 ```javascript
 var incrmmape = require( '@stdlib/stats/incr/mmape' );
 ```
 
 #### incrmmape( window )
 
 Returns an accumulator `function` which incrementally computes a moving [mean absolute percentage error][mean-absolute-percentage-error]. The `window` parameter defines the number of values over which to compute the moving [mean absolute percentage error][mean-absolute-percentage-error].
 
 ```javascript
 var accumulator = incrmmape( 3 );
 ```
 
 #### accumulator( \[f, a] )
 
 If provided input values `f` and `a`, the accumulator function returns an updated [mean absolute percentage error][mean-absolute-percentage-error]. If not provided input values `f` and `a`, the accumulator function returns the current [mean absolute percentage error][mean-absolute-percentage-error].
 
 ```javascript
 var accumulator = incrmmape( 3 );
 
 var m = accumulator();
 // returns null
 
 // Fill the window...
 m = accumulator( 2.0, 3.0 ); // [(2.0,3.0)]
 // returns ~33.33
 
 m = accumulator( 1.0, 4.0 ); // [(2.0,3.0), (1.0,4.0)]
 // returns ~54.17
 
 m = accumulator( 3.0, 9.0 ); // [(2.0,3.0), (1.0,4.0), (3.0,9.0)]
 // returns ~58.33
 
 // Window begins sliding...
 m = accumulator( 7.0, 3.0 ); // [(1.0,4.0), (3.0,9.0), (7.0,3.0)]
 // returns ~91.67
 
 m = accumulator( 5.0, 3.0 ); // [(3.0,9.0), (7.0,3.0), (5.0,3.0)]
 // returns ~88.89
 
 m = accumulator();
 // returns ~88.89
 ```
 
 </section>
 
 <!-- /.usage -->
 
 <section class="notes">
 
 ## 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.  
 
 -   **Warning**: the [mean absolute percentage error][mean-absolute-percentage-error]  has several shortcomings: 
 
     -   The measure is **not** suitable for intermittent demand patterns (i.e., when `a_i` is `0`).
     -   The [mean absolute percentage error][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.
 
 </section>
 
 <!-- /.notes -->
 
 <section class="examples">
 
 ## Examples
 
 <!-- eslint no-undef: "error" -->
 
 ```javascript
 var randu = require( '@stdlib/random/base/randu' );
 var incrmmape = require( '@stdlib/stats/incr/mmape' );
 
 var accumulator;
 var v1;
 var v2;
 var i;
 
 // Initialize an accumulator:
 accumulator = incrmmape( 5 );
 
 // For each simulated datum, update the moving 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() );
 ```
 
 </section>
 
 <!-- /.examples -->
 
 <section class="links">
 
 [mean-absolute-percentage-error]: https://en.wikipedia.org/wiki/Mean_absolute_percentage_error
 
 </section>
 
 <!-- /.links -->