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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 0-1), 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. T I P : Here you can discuss and learn a lot more about forecasting and Exponential Smoothing. Compare also: Regression Analysis | Dynamic Regression | ARIMA | Operations Research | Game Theory |
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