Overview

Forecast Error Measures

• {% MAE %} - mean absolute error
  {% \frac{1}{n} \sum_n |e_t| %}
• {% MAPE %} - mean absolute percentage error
  {% \frac{1}{n} \sum_n |e_t|/d_t %}
• {% RMSE %} - root mean square error
  {% \sqrt{ \frac{1}{n} \sum_n |e_t| } %}
• {% RMSE %} - root mean square error as a percentage
  {% \sqrt{ \frac{1}{n} \sum_n |e_t| } / (\frac{1}{n} \sum d_t) %}

Using Reduction to Implement Error Measures

var mae = function(data, value, forecast){
  return data.reduce(function(total, current){
    return total + Math.abs(value(current)-forecast(current))
  }, 0);
}
					

let err = await import('/lib/statistics/error/v1.0.0/regression.js');
let test = err.mae(...);