Get the Mean Absolute Percentage Error (MAPE) to evaluate to forecast accuracy of the cost predictions made by Earned Value Management data.

The MAPE is equal to the average absolute deviation between all EAC(t) forecasts and the final project cost. Cost predictions can be done by eigth forecasting methods and are explained in P2Tracking:get_eac_cost. The PME values are only available when the evm parameter of the P2Simulator:run_simulation has been set to 1 during its execution.

__Warning:__ The MAPE values are average MAPE values for all simulation runs and not for an individual simulation run.

Parameters:

I/O |
Type |
Name |
Description |
---|---|---|---|

output | float | PFNO | Avg_MAPE_EAC |

output | float | PF1 | Avg_MAPE_EAC_PF1 |

output | float | PFCPI | Avg_MAPE_EAC_PFCPI |

output | float | PFSPI | Avg_MAPE_EAC_PFSPI |

output | float | PFSPIT | Avg_MAPE_EAC_PFSPIT |

output | float | PFSCI | Avg_MAPE_EAC_PFSCI |

output | float | PFSCIT | Avg_MAPE_EAC_PFSCIT |

output | float | PFweightedSPICPI | Avg_MAPE_EAC_PFweightedSPICPI |

output | float | PFweightedSPITCPI | Avg_MAPE_EAC_PFweightedSPITCPI |

Learn More:

Step inside PMKC.

Example:

function ()

io.write('\n')

end

io.write('\n')

end

Class:

P2Simulator