maape

HydroErr.HydroErr.maape(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).

../_images/MAAPE.png

Range: 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.

Notes: Represents the mean absolute error as a percentage of the observed values. Handles 0s in the observed data. This metric is not as biased as MAPE by under-over predictions.

Parameters:
  • simulated_array (one dimensional ndarray) – An array of simulated data from the time series.
  • observed_array (one dimensional ndarray) – An array of observed data from the time series.
  • replace_nan (float, optional) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • replace_inf (float, optional) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • remove_neg (boolean, optional) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
  • remove_zero (boolean, optional) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
Returns:

The mean arctangent absolute percentage error.
Return type:

float

Examples

>>> import HydroErr as he
>>> import numpy as np

>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.mape(sim, obs)
11.639226612630866

References

  • Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting 32(3) 669-679.