A Comprehensive Exploration into Forecast Accuracy %

Forecast Accuracy is a critical Key Performance Indicator (KPI) that shows, as a percentage, how closely your Forecast aligns with the Actual value. Depending on the measurement objective—whether you want to assess the direction (Bias) of the error or just the magnitude (Size) of the error—this metric is utilized in two distinct ways.

Forecast Accuracy : Directional accuracy vs. Absolute accuracy


 

1. Directional Accuracy: Features and Calculation


This method focuses on diagnosing the bias or direction of the error by looking at the ratio between the forecast and the actual value.

Accuracy(%) = (Actual/Forecast) * 100

Accuracy% meaning by 3 types range : 100% , >100%, <100%

This metric is key to identifying the error's direction: whether the forecast was higher than the actual (over-forecast) or lower than the actual (under-forecast).
  •  Bias Analysis: By analyzing the error's direction (>100% or <100%), it is highly useful for diagnosing consistent tendencies where the forecasting model or process perpetually under- or over-forecasts.
  • Stability Assessment: This value serves as the base data for CV (Coefficient of Variation) analysis and is suitable for measuring the variation in the forecast, thereby assessing how consistent and stable the prediction is.

 

2. Absolute Accuracy: Features and Calculation


Absolute Accuracy completely ignores the error's direction (over or under) and evaluates only how close the forecast was to the actual value (the magnitude of the error).

Accuracy(%) =( 1- (Actual-Forecast) / forecast ) * 100

This formula uses the concept of the error rate (the percentage by which the forecast was wrong) and defines accuracy as 100% minus that error rate.
  • Example: If Actual = 110 and Forecast = 100
    • Absolute Error = |110 - 100| = 10
    • Error Rate = 10 / 100 = 0.1 --> 10%
    • Accuracy = (1 - 0.1) * 100 = 90%
    • If Actual = 90 and Forecast = 100, the Absolute Error is still 10, so the Accuracy is still 90%.
  • Key Feature & Purpose: It reflects only the magnitude of the forecast error, whether it was an over- or under-forecast, providing a simple, intuitive assessment of overall forecast closeness. Absolute Accuracy is typically paired with MAPE (Mean Absolute Percentage Error).


 

3. Core Differences and Utilization


The two methods differ not only in the resulting figures but also in the entire perspective on evaluating forecast performance.

Core difference and utilization : error reflected , core objrective, example


 4. Why Use (Actual ÷ Forecast) × 100 for CV Analysis?


The Coefficient of Variation (CV) is a metric used to assess the dispersion, or the stability and consistency, of data. It is essential to use Directional Accuracy when calculating the CV for forecast accuracy.
  • Necessity: The directional nature (whether the forecast was too high [<100\%] or too low [>100\%]) must be reflected to determine how consistently the forecasts are 'wobbling' above and below the 100\% target.
  • Therefore, to evaluate the stability (consistency) of forecast accuracy, it is appropriate to use the directional formula
Accuracy = actual /forecast * 100
which includes the error's direction.

 

5. Wrapping up


Ultimately, Accuracy % is a core metric indicating how close a forecast is to the actual result. We evaluate 'how close the forecast was (magnitude)' using Absolute Accuracy, and we assess 'how consistently stable the forecast is (direction)' through the CV analysis of Directional Accuracy.





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