There are many ways one might use Exponential Smoothing. Basically, Exponential Smoothing is a simple calculation one uses to collect data that allows one to predict future events.

There are a few good books on it actually. You should look it up.

1) forecasting for stationary series

A- Moving average

B- Exponential Smoothing

2) For Trends

A- Regression

B- Double Exponential Smoothing

3) for Seasonal Series

A- Seasonal factor

B- Seasonal Decomposition

C- Winters's methode

When implemented digitally, exponential smoothing is easier to implement and more efficient to compute, as it does not require maintaining a history of previous input data values. Furthermore, there are no sudden effects in the output as occurs with a moving average when an outlying data point passes out of the interval over which you are averaging. With exponential smoothing, the effect of the unusual data fades uniformly. (It still has a big impact when it first appears.)

Exponential Smoothing Model