Exponential SmoothingKnowledge Center 
Large Scale Statistical Forecasting. Explanation of Exponential Smoothing. 
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Nobody can really look into the future. However modern statistical methods, econometric models and Business Intelligence software can indeed to some extent help companies forecast and estimate what is going to happen in the future. The Exponential Smoothing modelThe Exponential Smoothing (ESM) 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. ESM is often used on Large Scale Statistical Forecasting problems, because it is both robust and easy to apply. ESM is a popular scheme to create a smoothed Time Series. Whereas
in Single Moving Averages the past observations are weighted equally, Exponential
Smoothing assigns exponentially decreasing weights if the observation gets
older. In other words: recent observations are given more weight in forecasting
than the older observations.
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