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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 model
The 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 0-1),
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.
In the case of moving averages, the weights assigned to the observations are
the same and are equal to 1/N. In Exponential Smoothing, however, there are
one or more smoothing parameters which must be determined or estimated. These
choices determine the weights assigned to the observations.
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4 Exponential Smoothing Method Variants
When to use What Formula? Examples. PROs and CONs....
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Compare also: Regression Analysis
| Dynamic Regression
| ARIMA |
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Game Theory |
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