Forecasting is widely used in management and Excel allows us to solve simple forecasting tasks. Exponential smoothing is one of the most common techniques that is applied to time series data to make forecasts. It includes allpast observations and weighs recent observations much more heavily than older observations. Exceldummies.com teaches how this model is mainly used for smoothing and short-term forecasts.

Exponential Smoothing Formula

The equation to calculate an exponential smoothing is:

F_t= α A_t-1+ (1- α) Ft_-1

where

F_t – Forecast for the period t.
A_t-1 Actual value of the time-series in the prior period,
F_t-1Forecast made for the prior period,
α is the smoothing factor, and 0 < α < 1

This method combines the previous forecast with a weighted error measurement. The error comes from the difference between the prior forecast and actual observation at the time of the forecast. A forecast is made by revising the current forecast. The errors of the earlier forecast are checked so that corrections can be made in the new forecast.

Check out Exceldummies.com for Exponential Smoothing tutorial with the real life examples and FREE resources.