REVISEDBESTFIT_FORECAST

REVISEDBESTFIT_FORECAST is an enhanced forecasting technique that selects and applies the most suitable forecasting model to improve forecast accuracy.

Logic Used for Revised Best Fit

  1. Retrieve the last 12 months of sales history for the selected product and region.

    Note: The 12-month period is currently hardcoded.

  2. Determine the minimum and maximum sales values from the 12-month history.

  3. Calculate the acceptable forecast range using the Percentage configured in the Data Measure Parameters.

Formula:

Lower Bound = Minimum Sales − (Minimum Sales × Percentage) Upper Bound = Maximum Sales + (Maximum Sales × Percentage)

Example:

  • Minimum Sales = 80
  • Maximum Sales = 200
  • Percentage = 20%

Lower Bound = 80 − (80 × 20%) = 64 Upper Bound = 200 + (200 × 20%) = 240

  1. Sort the available Data Measures in ascending order of RMSE (based on the Error Parameter configured for Best Fit).

  2. Starting with the data measure that has the lowest RMSE, verify whether the forecast value for the first forecast period falls within the calculated range.

  3. If the forecast value lies within the acceptable range, that data measure is selected as the Revised Best Fit.

  4. If the forecast value is outside the acceptable range, evaluate the next data measure in RMSE order and repeat the validation until a suitable data measure is found.

Selection Criteria

A data measure is selected as Revised Best Fit only when:

  • It has the lowest available RMSE among the remaining candidates.
  • Its first forecasted value falls within the calculated lower and upper bounds.