Exponential Smoothing 
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Summary of Exponential Smoothing (ES). Abstract 
Although nobody can really look into the future, modern statistical methods, econometric models and business intelligence software go a long way in helping businesses forecast and estimate what is going to happen in the future. The ES model uses a weighted average of past and current values, adjusting weight on current values to account for the effects of swings in the data, such as seasonality. Using an alpha term (between 01), you can adjust the sensitivity of the smoothing effects. ES is often used on Large Scale Statistical Forecasting problems, because it is both robust and easy to apply.
ES is a popular
scheme to produce a smoothed Time Series. Whereas in Single Moving
Averages the past observations are weighted equally, Exponential Smoothing
assigns exponentially decreasing weights as the observation get older. In
other words: recent observations are given relatively more weight in
forecasting than the older observations. Compare also: Regression Analysis  Dynamic Regression  ARIMA  Operations Research  Game Theory 
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