http://home.ubalt.edu/ntsbarsh/statdata/Forecast.htm
Exponential Smoothing Techniques: One of the most successful
forecasting methods is the exponential smoothing (ES) techniques.
Moreover, it can be modified efficiently to use effectively for time series
with seasonal patterns. It is also easy to adjust for past errors-easy to
prepare follow-on forecasts, ideal for situations where many forecasts
must be prepared, several different forms are used depending on presence
of trend or cyclical variations. In short, an ES is an averaging technique
that uses unequal weights; however, the weights applied to past
observations decline in an exponential manner.
Single Exponential Smoothing: It calculates the smoothed series as a
damping coefficient times the actual series plus 1 minus the damping
coefficient times the lagged value of the smoothed series. The extrapolated
smoothed series is a constant, equal to the last value of the smoothed
series during the period when actual data on the underlying series are
available. While the simple Moving Average method is a special case of
the ES, the ES is more parsimonious in its data usage.
Ft+1 =  Dt + (1 - ) Ft
where:




Dt is the actual value
Ft is the forecasted value
 is the weighting factor, which ranges from 0 to 1
t is the current time period.
Notice that the smoothed value becomes the forecast for period t + 1.
A small  provides a detectable and visible smoothing. While a large 
provides a fast response to the recent changes in the time series but
provides a smaller amount of smoothing. Notice that the exponential
smoothing and simple moving average techniques will generate forecasts
having the same average age of information if moving average of order n
is the integer part of (2-)/.
An exponential smoothing over an already smoothed time series is called
double-exponential smoothing. In some cases, it might be necessary to
extend it even to a triple-exponential smoothing. While simple
exponential smoothing requires stationary condition, the doubleexponential smoothing can capture linear trends, and triple-exponential
smoothing can handle almost all other business time series.