Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

forecast bias formula apics | 0.7 | 0.9 | 5509 | 46 | 27 |

forecast | 0.84 | 0.8 | 222 | 75 | 8 |

bias | 0.7 | 0.7 | 3763 | 61 | 4 |

formula | 1.57 | 0.7 | 6600 | 66 | 7 |

apics | 1.44 | 0.7 | 1825 | 47 | 5 |

It is an average of non-absolute values of forecast errors. If it is negative, a company tends to over-forecast; if positive, it tends to under-forecast. In the machine learning context, bias is how a forecast deviates from actuals. In new product forecasting, companies tend to over-forecast.

Rick Glover on LinkedIn described his calculation of BIAS this way: Calculate the BIAS at the lowest level (for example, by product, by location) as follows: BIAS = Historical Forecast Units (Two-months frozen) minus Actual Demand Units. If the forecast is greater than actual demand than the bias is positive (indicates over-forecast).

Their forecast is therefore biased based on the anecdotes. Recent data bias: This is probably true for all processes where humans are involved. The more recent occurrences weigh heavier in our mind. In the case of forecasting, this can create an overreaction based on the latest events.

A forecast history entirely void of bias will return a value of zero, with 12 observations, the worst possible result would return either +12 (under-forecast) or -12 (over-forecast). Such a forecast history returning a value greater than 4.5 or less than negative 4.5 would be considered out of control.