The simplest form of exponential smoothing is given by the formulae:

Triple exponential smoothing takes into account seasonal changes as well as trends.

Exponential smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements.

Exponential smoothing would again normally be used for short term forecasting, for periods up to six months ahead.

Simple exponential smoothing is easily applied, and it produces a smoothed statistic as soon as two observations are available.

Exponential smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts.

For more information see: About Auctions Exponential Smoothing paper (pdf)

Whereas in the simple moving average the past observations are weighted equally, exponential smoothing assigns exponentially decreasing weights over time.

Exponential smoothing used to reduce irregularities (random fluctuations) in time series data, thus providing a clearer view of the true underlying behaviour of the series.

They differ in that exponential smoothing takes into account all past data, whereas moving average only takes into account "k" past data points.

Exponential smoothing is used in the projections for elementary and secondary enrollments, high school graduates, new teacher hires, postsecondary degree-granting enrollments, and degrees conferred.