The benefits of measuring seasonality of accuracy are that users can plan high/low scenarios for each prediction based on the season-specific range of possibilities. Departments can adjust how much capacity, safety stock, or cash reserves are needed based on their own worst case. And it's easier to determine missing factors for model improvement when you focus on adding predictors to the high noise areas of the model.
Nate Kaemingk
Nate is a Fractional CFO for two Montana based companies and Chief Forecaster for BetterForecasting.com.
Nate started his career as a mechanical engineer using inferential statistics to model chemical reactions in Diesel engines. Later, during his MBA, the inferential statistics background quickly became advantageous in forecasting and scenario analysis. Nate has had the opportunity to apply these methods in roles with multiple Fortune 500 companies, and subsequently as CFO and Chief Forecaster.
Nate lives in a suburb of Atlanta with his wife, daughter, and four dogs.
Author's Articles
December 13, 2021
The benefits of measuring seasonality of accuracy are that users can plan high/low scenarios for each prediction based on the season-specific range of possibilities. Departments can adjust how much capacity, safety stock, or cash reserves are needed based on their own worst case. And it's easier to determine missing factors for model improvement when you focus on adding predictors to the high noise areas of the model.