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Nate Kaemingk

Nate Kaemingk

Nate is the Chief Forecaster for BetterForecasting.com, who has launched a Forecasting Copilot for finance teams!

Nate started his career as a mechanical engineer using inferential statistics to build models that earned seven patents.  Later, during his MBA, his 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, two daughters, and several dogs.

Author's Articles

Nate-Kaemingk-Forecasting-Planning
Forecasting vs Planning: Why Mixing Them Fails in FP&A
June 24, 2025
FP&A Tags:
Modelling and Forecasting, Planning and Budgeting, Rolling Forecast, Continuous Planning

In this article, discover how clearly separating rolling forecasts from continuous planning unlocks the real value of AI/ML in FP&A.

Read more
The-Drivers-Of-Inflation-2022-by-Nate-Kaemingk-Main-Picture
Drivers of Inflation in 2022, and How FP&A Can Prepare for It
October 11, 2022
FP&A Tags:
Modelling and Forecasting, FP&A Scenario Planning, Planning and Budgeting

Inflation and recession are creating chaos for most businesses in the world right now. In this article, we will explore some of the macroeconomic theories driving the current inflationary environment. These theories will allow you to understand, model, and advise your company on its options for addressing chaos and uncertainty.

Read more
forecast
Caution: Forecast Accuracy is Seasonal Too!
December 13, 2021
FP&A Tags:
Modelling and Forecasting, Forecasting Quality

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.

Read more
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