Single Exponential Smoothing is a procedure that repeats enumeration continually by using the newest data. This method can be used if the data is not significantly influenced by trend and seasonal factor.

To smooth the data with single exponential smoothing requires a parameter called the smoothing constant (). Each data point is given a certain weighting, for the newest data, (1-) for older data and etc. The value of must be between 0 and 1. The following is the equation of smoothed value:

By doing a simple substitution, the equation above can be written as:

Forecasting value

Forecasting with single exponential smoothing can be done by substituting this equation:

The equation above also can be written in the following way:

where is the forecasting error for n period. From this equation, we can see that the forecasting resulted with this method is the last forecasted value added with an adjustment for error in the last forecasted value.

Starting value

Practically, to calculate the smoothing statistic at the first observation , we can use the equation . Then it is substituted into the smoothing statistic equation to calculate , and the smoothing process is continued until we get value. To calculate the equation above, a starting value is needed. can be calculated from the average of several observations. The first several observations can be chosen to determine .